From 6dd19b3e03cdcfaa5b2f8fce2ee691b201b762f1 Mon Sep 17 00:00:00 2001 From: ppalmes Date: Wed, 31 May 2023 02:00:30 +0100 Subject: [PATCH 01/12] add support to offline RL Signed-off-by: ppalmes --- examples/AutoOfflineRL/CondaPkg.toml | 7 + examples/AutoOfflineRL/LICENSE | 21 ++ examples/AutoOfflineRL/Project.toml | 25 ++ examples/AutoOfflineRL/README.md | 0 examples/AutoOfflineRL/data/smalldata.parquet | Bin 0 -> 215046 bytes examples/AutoOfflineRL/src/AutoOfflineRL.jl | 51 ++++ examples/AutoOfflineRL/src/offlinerls.jl | 219 ++++++++++++++++++ examples/AutoOfflineRL/src/utils.jl | 35 +++ examples/AutoOfflineRL/test/runtests.jl | 58 +++++ 9 files changed, 416 insertions(+) create mode 100644 examples/AutoOfflineRL/CondaPkg.toml create mode 100644 examples/AutoOfflineRL/LICENSE create mode 100644 examples/AutoOfflineRL/Project.toml create mode 100644 examples/AutoOfflineRL/README.md create mode 100644 examples/AutoOfflineRL/data/smalldata.parquet create mode 100644 examples/AutoOfflineRL/src/AutoOfflineRL.jl create mode 100644 examples/AutoOfflineRL/src/offlinerls.jl create mode 100644 examples/AutoOfflineRL/src/utils.jl create mode 100644 examples/AutoOfflineRL/test/runtests.jl diff --git a/examples/AutoOfflineRL/CondaPkg.toml b/examples/AutoOfflineRL/CondaPkg.toml new file mode 100644 index 00000000..e3385880 --- /dev/null +++ b/examples/AutoOfflineRL/CondaPkg.toml @@ -0,0 +1,7 @@ +[deps] +scikit-learn = "" +pandas = "" +numpy = "" + +[pip.deps] +d3rlpy = "" diff --git a/examples/AutoOfflineRL/LICENSE b/examples/AutoOfflineRL/LICENSE new file mode 100644 index 00000000..554ba210 --- /dev/null +++ b/examples/AutoOfflineRL/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Paulito Palmes, PhD and contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/examples/AutoOfflineRL/Project.toml b/examples/AutoOfflineRL/Project.toml new file mode 100644 index 00000000..8aae597e --- /dev/null +++ b/examples/AutoOfflineRL/Project.toml @@ -0,0 +1,25 @@ +name = "AutoOfflineRL" +uuid = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" +authors = ["Paulito Palmes, PhD "] +version = "0.1.0" + +[deps] +AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6" +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" +Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" +MicroMamba = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea" +Parquet = 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CrossValidators +using AMLPipelineBase: NARemovers + +export Machine, Learner, Transformer, Workflow, Computer +export holdout, kfold, score, infer_eltype, nested_dict_to_tuples, + nested_dict_set!, nested_dict_merge, create_transformer, + mergedict, getiris,getprofb, + skipmean,skipmedian,skipstd, + aggregatorclskipmissing +export Baseline, Identity +export Imputer,OneHotEncoder,Wrapper +export PrunedTree,RandomForest,Adaboost +export VoteEnsemble, StackEnsemble, BestLearner +export FeatureSelector, CatFeatureSelector, NumFeatureSelector, CatNumDiscriminator +export crossvalidate +export NARemover +export @pipeline +export @pipelinex +export @pipelinez +export +, |>, *, |, >> +export Pipeline, ComboPipeline + + + +#export RLMachine, RLOffline, WeeklyEpisodes + + +#Base.@kwdef struct WeeklyEpisodes <: RLMachine +# _params::Dict = Dict() +# _model::Dict = Dict() +#end + +include("offlinerls.jl") +using .OfflineRLs +export DiscreteRLOffline, fit! +export driver, listdiscreateagents + + + +end # module diff --git a/examples/AutoOfflineRL/src/offlinerls.jl b/examples/AutoOfflineRL/src/offlinerls.jl new file mode 100644 index 00000000..2d5db864 --- /dev/null +++ b/examples/AutoOfflineRL/src/offlinerls.jl @@ -0,0 +1,219 @@ +module OfflineRLs + +using Distributed +using DataFrames +using Random +using CSV +using Dates + +using ..AbsTypes +using ..Utils +import ..AbsTypes: fit, fit!, transform, transform! + +import PythonCall +const PYC = PythonCall + +using ..Utils: nested_dict_merge + +export DiscreteRLOffline, fit!, transform! +export listdiscreateagents, driver + +const rl_dict = Dict{String, PYC.Py}() + +const PYRL = PYC.pynew() +const PYPD = PYC.pynew() +const PYNP = PYC.pynew() +const PYDT = PYC.pynew() + +function __init__() + PYC.pycopy!(PYRL, PYC.pyimport("d3rlpy.algos")) + PYC.pycopy!(PYDT, PYC.pyimport("d3rlpy.dataset")) + PYC.pycopy!(PYPD, PYC.pyimport("pandas")) + PYC.pycopy!(PYNP, PYC.pyimport("numpy")) + + # OfflineRLs + rl_dict["DiscreteBC"] = PYRL + rl_dict["DQN"] = PYRL + rl_dict["NFQ"] = PYRL + rl_dict["DoubleDQN"] = PYRL + rl_dict["DiscreteBCQ"] = PYRL + rl_dict["DiscreteCQL"] = PYRL + rl_dict["DiscreteSAC"] = PYRL + #rl_dict["DiscreteRandomPolicy"] = PYRL +end + +mutable struct DiscreteRLOffline <: Learner + name::String + model::Dict{Symbol,Any} + + function DiscreteRLOffline(args=Dict{Symbol,Any}()) + default_args = Dict{Symbol,Any}( + :name => "DQN", + :tag => "RLOffline", + :rlagent => "DQN", + :iterations => 100, + :epochs => 10000, + :save_metrics => false, + :rlobjtrained => PYC.PyNULL, + :o_header => ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"], + :a_header => ["action"], + :r_header => ["reward"], + :save_model => true, + :impl_args => Dict{Symbol,Any}( + :scaler => "min_max", + :use_gpu => false + ) + ) + cargs = nested_dict_merge(default_args,args) + datestring = Dates.format(now(), "yyyy-mm-dd-HH-MM") + cargs[:name] = cargs[:name]*"_"*datestring + rlagent = cargs[:rlagent] + if !(rlagent in keys(rl_dict)) + println("error: $rlagent is not supported.") + println() + discreteagents() + error("Argument keyword error") + end + new(cargs[:name],cargs) + end +end + +function DiscreteRLOffline(rlagent::String, args::Dict) + DiscreteRLOffline(Dict(:rlagent => rlagent, :name => rlagent, args...)) +end + +function DiscreteRLOffline(rlagent::String; args...) + DiscreteRLOffline(Dict( + :rlagent => rlagent, + :name => rlagent, + args... + )) +end + +function listdiscreateagents() + println() + println("RL Discrete Agents:") + agents = keys(rl_dict) |> collect + [println(" ",agent," ") for agent in agents] + println("See d3rlpy python package for details about the agent arguments.") + nothing +end + +function discreteagents() + println() + println("syntax: DiscreteRLOffline(name::String, args::Dict)") + println("and 'args' are the agent's parameters") + println("See d3rlpy python package for details about the agent arguments.") + println("use: listdiscreateagents() to get the available RL agents") +end + +function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame) + o_header = agent.model[:o_header] + a_header = agent.model[:a_header] + r_header = agent.model[:r_header] + _observations = df[:, o_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _actions = df[:, a_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _rewards = df[:, r_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + ## inject end of data by terminal column + nrow, _ = size(df) + _terminals = zeros(Int, nrow) + _terminals[end] = 1 + _terminals = _terminals .|> PYC.float |> x -> PYNP.array(x, dtype = "int32") + ## create dataset for RLOffline + mdp_dataset = PYDT.MDPDataset( + observations = _observations, + actions = _actions, + rewards = _rewards, + terminals = _terminals, + ) + ## save params + agent.model[:mdp_dataset] = mdp_dataset + agent.model[:np_observations] = _observations + agent.model[:np_actions] = _actions + agent.model[:np_rewards] = _rewards + agent.model[:o_header] = o_header + agent.model[:a_header] = a_header + agent.model[:r_header] = r_header + return mdp_dataset +end + +function checkheaders(agent::DiscreteRLOffline, df) + o_header = agent.model[:o_header] + a_header = agent.model[:a_header] + r_header = agent.model[:r_header] + dfnames = names(df) + [@assert header in dfnames "\"$header\" is not in data header" + for header in vcat(o_header, a_header, r_header)] +end + +function fit!(agent::DiscreteRLOffline, df::AbstractDataFrame, v::Vector=[]; runtime_args...)::Nothing + # check if headers exist + checkheaders(agent::DiscreteRLOffline, df) + # create mdp data + nrow, ncol = size(df) + mdp_dataset = createmdpdata!(agent, df) + ## prepare algorithm + logging = agent.model[:save_metrics] + impl_args = copy(agent.model[:impl_args]) + rlagent = agent.model[:rlagent] + py_rlagent = getproperty(rl_dict[rlagent],rlagent) + pyrlobj = py_rlagent(;impl_args...) + pyrlobj.fit(mdp_dataset; save_metrics = logging, runtime_args... ) + ## save rl to model dictionary + agent.model[:rlobjtrained] = pyrlobj + agent.model[:nrow] = nrow + agent.model[:ncol] = ncol + ## save model to file + if agent.model[:save_model] == true + path = pkgdir(AutoOfflineRL) + agentname = agent.model[:name] + tag = agent.model[:tag] + fnmodel = "$path/model/$(agentname)_$(tag)_model.pt" + fnpolicy = "$path/model/$(agentname)_$(tag)_policy.pt" + pyrlobj.save_model(fnmodel) + pyrlobj.save_policy(fnpolicy) + end + return nothing +end + + +function transform!(agent::DiscreteRLOffline,df::DataFrame=DataFrame())::Vector + pyrlobj = agent.model[:rlobjtrained] + o_header = agent.model[:o_header] + observations = df[:, o_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + res = map(observations) do obs + action = pyrlobj.predict(obs) + value = pyrlobj.predict_value([obs],action) + action = PYC.pyconvert.(Float64,action) + value = PYC.pyconvert.(Float64,value) + obs = PYC.pyconvert.(Float64,obs) + (;obs,action,value) + end + return res +end + +function driver() + #dataset = ENV["HOME"]*"/phome/ibmgithub/ZOS/data/processed_batch.csv" + path = pkgdir(AutoOfflineRL) + dataset = "$path/data/smalldata.csv" + df = CSV.read(dataset, DataFrame) + #for agentid in reverse([keys(rl_dict)...]) + # println(agentid) + # if agentid != "DiscreteRandomPolicy" + # agent = DiscreteRLOffline(agentid,Dict(:iterations=>1,:epochs=>10000)) + # fit!(agent,df) + # end + #end + #discreteagents() + agent = DiscreteRLOffline("DoubleDQN"; tag="sac") + header = agent.model[:o_header] + fit!(agent,df; n_epochs=1) + transform(agent,df[1:1,:]) + #vec = df[1,header] |> Vector + #transform(agent,vec) + #m = DiscreteRLOffline() + #dqn_agent(m,dat) +end + + +end diff --git a/examples/AutoOfflineRL/src/utils.jl b/examples/AutoOfflineRL/src/utils.jl new file mode 100644 index 00000000..886fcf12 --- /dev/null +++ b/examples/AutoOfflineRL/src/utils.jl @@ -0,0 +1,35 @@ +module Utils + +export nested_dict_merge + +""" + nested_dict_merge(first::Dict, second::Dict) + +Second nested dictionary is merged into first. + +If a second dictionary's value as well as the first +are both dictionaries, then a merge is conducted between +the two inner dictionaries. +Otherwise the second's value overrides the first. + +- `first`: first nested dictionary +- `second`: second nested dictionary + +Returns: merged nested dictionary +""" +function nested_dict_merge(first::Dict, second::Dict) + target = copy(first) + for (second_key, second_value) in second + values_both_dict = + typeof(second_value) <: Dict && + typeof(get(target, second_key, nothing)) <: Dict + if values_both_dict + target[second_key] = nested_dict_merge(target[second_key], second_value) + else + target[second_key] = second_value + end + end + return target +end + +end diff --git a/examples/AutoOfflineRL/test/runtests.jl b/examples/AutoOfflineRL/test/runtests.jl new file mode 100644 index 00000000..d5733ef5 --- /dev/null +++ b/examples/AutoOfflineRL/test/runtests.jl @@ -0,0 +1,58 @@ +module TestOfflineRL +using AutoOfflineRL +using Test +using DataFrames +using PythonCall +using Parquet +const PYC=PythonCall + +@testset "Load Agents with Default Params" begin + for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) + @info "loading $agentid default params" + rlagent = DiscreteRLOffline(agentid) + @test typeof(rlagent) <: AutoOfflineRL.Learner + end +end + + +@testset "Load Agents with Param Args" begin + println() + for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) + @info "loading $agentid with customized params" + rlagent = DiscreteRLOffline(agentid, + Dict(:name=>agentid, + :iterations=>10000, + :epochs=>100) + ) + @test typeof(rlagent) <: AutoOfflineRL.Learner + end +end + +@testset "Test Exceptions" begin + @test_throws ErrorException DiscreteRLOffline("dummy") +end + +@testset "Test Agent fit!/transform Runs" begin + println() + path = pkgdir(AutoOfflineRL) + dataset = "$path/data/smalldata.parquet" + df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing + for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) + @info "training $agentid" + agent = DiscreteRLOffline(agentid; save_model=false) + o_header = agent.model[:o_header] + fit!(agent,df; n_epochs=1,verbose=false, show_progress=true) + @test agent.model[:rlobjtrained] !== PYC.PyNULL + @info "transform $agentid" + adf = df[1:2,:] + if agentid != "DiscreteBC" + res = AutoOfflineRL.transform!(agent,adf) + @test typeof(res[1]) .== NamedTuple{(:obs,:action, :value), Tuple{Vector{Float64},Vector{Float64}, Vector{Float64}}} + end + end +end + + + + +end From 22b3357b36414b51ae0c8452a0f1b632c7379595 Mon Sep 17 00:00:00 2001 From: ppalmes Date: Wed, 31 May 2023 03:05:16 +0100 Subject: [PATCH 02/12] minor edits to driver Signed-off-by: ppalmes --- examples/AutoOfflineRL/src/offlinerls.jl | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/examples/AutoOfflineRL/src/offlinerls.jl b/examples/AutoOfflineRL/src/offlinerls.jl index 2d5db864..d87dc84c 100644 --- a/examples/AutoOfflineRL/src/offlinerls.jl +++ b/examples/AutoOfflineRL/src/offlinerls.jl @@ -1,7 +1,9 @@ module OfflineRLs +using AutoOfflineRL +using Parquet using Distributed -using DataFrames +using DataFrames: DataFrame, dropmissing using Random using CSV using Dates @@ -146,7 +148,7 @@ function checkheaders(agent::DiscreteRLOffline, df) for header in vcat(o_header, a_header, r_header)] end -function fit!(agent::DiscreteRLOffline, df::AbstractDataFrame, v::Vector=[]; runtime_args...)::Nothing +function fit!(agent::DiscreteRLOffline, df::DataFrame, v::Vector=[]; runtime_args...)::Nothing # check if headers exist checkheaders(agent::DiscreteRLOffline, df) # create mdp data @@ -195,8 +197,8 @@ end function driver() #dataset = ENV["HOME"]*"/phome/ibmgithub/ZOS/data/processed_batch.csv" path = pkgdir(AutoOfflineRL) - dataset = "$path/data/smalldata.csv" - df = CSV.read(dataset, DataFrame) + dataset = "$path/data/smalldata.parquet" + df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing #for agentid in reverse([keys(rl_dict)...]) # println(agentid) # if agentid != "DiscreteRandomPolicy" @@ -208,7 +210,7 @@ function driver() agent = DiscreteRLOffline("DoubleDQN"; tag="sac") header = agent.model[:o_header] fit!(agent,df; n_epochs=1) - transform(agent,df[1:1,:]) + transform!(agent,df[1:20,:]) #vec = df[1,header] |> Vector #transform(agent,vec) #m = DiscreteRLOffline() From ee5aea64091e3cf8fa350bee3fb803c8cebff054 Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 13 Jun 2023 14:40:53 +0100 Subject: [PATCH 03/12] fixes for fit function Signed-off-by: ppalmes --- Project.toml | 1 + examples/AutoOfflineRL/examples/Project.toml | 6 +++ examples/AutoOfflineRL/examples/script.jl | 39 ++++++++++++++++++++ examples/AutoOfflineRL/src/offlinerls.jl | 39 +++++++++----------- 4 files changed, 64 insertions(+), 21 deletions(-) create mode 100644 examples/AutoOfflineRL/examples/Project.toml create mode 100644 examples/AutoOfflineRL/examples/script.jl diff --git a/Project.toml b/Project.toml index 4144adb5..89753180 100644 --- a/Project.toml +++ b/Project.toml @@ -7,6 +7,7 @@ version = "0.4.0" AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6" CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +MicroMamba = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" diff --git a/examples/AutoOfflineRL/examples/Project.toml b/examples/AutoOfflineRL/examples/Project.toml new file mode 100644 index 00000000..2a65d78c --- /dev/null +++ b/examples/AutoOfflineRL/examples/Project.toml @@ -0,0 +1,6 @@ +[deps] +AutoMLPipeline = "08437348-eef5-4817-bc1b-d4e9459680d6" +AutoOfflineRL = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +MicroMamba = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea" +Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" diff --git a/examples/AutoOfflineRL/examples/script.jl b/examples/AutoOfflineRL/examples/script.jl new file mode 100644 index 00000000..82f6d9f1 --- /dev/null +++ b/examples/AutoOfflineRL/examples/script.jl @@ -0,0 +1,39 @@ +using AutoOfflineRL +using AutoMLPipeline +using Parquet +using DataFrames + +# load preprocessing elements +#### Scaler +rb = SKPreprocessor("RobustScaler"); +pt = SKPreprocessor("PowerTransformer"); +norm = SKPreprocessor("Normalizer"); +mx = SKPreprocessor("MinMaxScaler"); +std = SKPreprocessor("StandardScaler") +disc = CatNumDiscriminator(); +#### categorical preprocessing +ohe = OneHotEncoder(); +#### Column selector +catf = CatFeatureSelector(); +numf = NumFeatureSelector(); +# load filters +#### Decomposition +apca = SKPreprocessor("PCA",Dict(:autocomponent=>true)); +pca = SKPreprocessor("PCA"); +afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true)); +fa = SKPreprocessor("FactorAnalysis"); +aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true)); +ica = SKPreprocessor("FastICA"); + +# load dataset +path = pkgdir(AutoOfflineRL) +dataset = "$path/data/smalldata.parquet" +df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing + +rlagent = DiscreteRLOffline(Dict(:name=>"NFQ",:rlagent=>"NFQ", + :runtime_args=>Dict(:n_epochs=>10))) + + +pipeline = (numf |> ica ) + (numf |> pca) |> rlagent + +fit_transform!(pipeline,df) diff --git a/examples/AutoOfflineRL/src/offlinerls.jl b/examples/AutoOfflineRL/src/offlinerls.jl index d87dc84c..63f003fa 100644 --- a/examples/AutoOfflineRL/src/offlinerls.jl +++ b/examples/AutoOfflineRL/src/offlinerls.jl @@ -17,7 +17,7 @@ const PYC = PythonCall using ..Utils: nested_dict_merge -export DiscreteRLOffline, fit!, transform! +export DiscreteRLOffline, fit!, transform!, fit, transform export listdiscreateagents, driver const rl_dict = Dict{String, PYC.Py}() @@ -54,16 +54,18 @@ mutable struct DiscreteRLOffline <: Learner :tag => "RLOffline", :rlagent => "DQN", :iterations => 100, - :epochs => 10000, :save_metrics => false, :rlobjtrained => PYC.PyNULL, :o_header => ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"], :a_header => ["action"], :r_header => ["reward"], - :save_model => true, + :save_model => false, + :runtime_args => Dict{Symbol, Any}( + :n_epochs => 5, + ), :impl_args => Dict{Symbol,Any}( :scaler => "min_max", - :use_gpu => false + :use_gpu => false, ) ) cargs = nested_dict_merge(default_args,args) @@ -109,13 +111,10 @@ function discreteagents() println("use: listdiscreateagents() to get the available RL agents") end -function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame) - o_header = agent.model[:o_header] - a_header = agent.model[:a_header] - r_header = agent.model[:r_header] - _observations = df[:, o_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - _actions = df[:, a_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - _rewards = df[:, r_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") +function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector) + _observations = df |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _actions = action_reward[1] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _rewards = action_reward[2] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") ## inject end of data by terminal column nrow, _ = size(df) _terminals = zeros(Int, nrow) @@ -133,9 +132,6 @@ function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame) agent.model[:np_observations] = _observations agent.model[:np_actions] = _actions agent.model[:np_rewards] = _rewards - agent.model[:o_header] = o_header - agent.model[:a_header] = a_header - agent.model[:r_header] = r_header return mdp_dataset end @@ -148,13 +144,14 @@ function checkheaders(agent::DiscreteRLOffline, df) for header in vcat(o_header, a_header, r_header)] end -function fit!(agent::DiscreteRLOffline, df::DataFrame, v::Vector=[]; runtime_args...)::Nothing +function fit!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector)::Nothing # check if headers exist - checkheaders(agent::DiscreteRLOffline, df) + #checkheaders(agent::DiscreteRLOffline, df) # create mdp data nrow, ncol = size(df) - mdp_dataset = createmdpdata!(agent, df) + mdp_dataset = createmdpdata!(agent, df,action_reward) ## prepare algorithm + runtime_args = agent.model[:runtime_args] logging = agent.model[:save_metrics] impl_args = copy(agent.model[:impl_args]) rlagent = agent.model[:rlagent] @@ -181,8 +178,8 @@ end function transform!(agent::DiscreteRLOffline,df::DataFrame=DataFrame())::Vector pyrlobj = agent.model[:rlobjtrained] - o_header = agent.model[:o_header] - observations = df[:, o_header] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + #o_header = agent.model[:o_header] + observations = df |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") res = map(observations) do obs action = pyrlobj.predict(obs) value = pyrlobj.predict_value([obs],action) @@ -202,14 +199,14 @@ function driver() #for agentid in reverse([keys(rl_dict)...]) # println(agentid) # if agentid != "DiscreteRandomPolicy" - # agent = DiscreteRLOffline(agentid,Dict(:iterations=>1,:epochs=>10000)) + # agent = DiscreteRLOffline(agentid) # fit!(agent,df) # end #end #discreteagents() agent = DiscreteRLOffline("DoubleDQN"; tag="sac") header = agent.model[:o_header] - fit!(agent,df; n_epochs=1) + fit!(agent,df) transform!(agent,df[1:20,:]) #vec = df[1,header] |> Vector #transform(agent,vec) From c43496263177f57d6c6a5d43c2d39f02514d3e18 Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 13 Jun 2023 14:45:15 +0100 Subject: [PATCH 04/12] fixes for fit function Signed-off-by: ppalmes --- examples/AutoOfflineRL/examples/script.jl | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/examples/AutoOfflineRL/examples/script.jl b/examples/AutoOfflineRL/examples/script.jl index 82f6d9f1..9d1af1fe 100644 --- a/examples/AutoOfflineRL/examples/script.jl +++ b/examples/AutoOfflineRL/examples/script.jl @@ -2,7 +2,10 @@ using AutoOfflineRL using AutoMLPipeline using Parquet using DataFrames +<<<<<<< Updated upstream +======= +>>>>>>> Stashed changes # load preprocessing elements #### Scaler rb = SKPreprocessor("RobustScaler"); @@ -30,6 +33,11 @@ path = pkgdir(AutoOfflineRL) dataset = "$path/data/smalldata.parquet" df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing +df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] +reward = df[:,["reward"]] |> deepcopy |> DataFrame +action = df[:,["action"]] |> deepcopy |> DataFrame +action_reward = DataFrame[action, reward] + rlagent = DiscreteRLOffline(Dict(:name=>"NFQ",:rlagent=>"NFQ", :runtime_args=>Dict(:n_epochs=>10))) @@ -37,3 +45,18 @@ rlagent = DiscreteRLOffline(Dict(:name=>"NFQ",:rlagent=>"NFQ", pipeline = (numf |> ica ) + (numf |> pca) |> rlagent fit_transform!(pipeline,df) + +p = @pipeline ((numf |> ica ) + (numf |> pca)) |> rlagent + +dfnew=fit_transform(p,df_input) + +|> rlagent +pipeline = rlagent + +fit!(p,df_input,action_reward) + +AutoOfflineRL.transform!(pipeline,df_input) + + + +a=fit_transform!(pipeline,df) From d8f268297961ab9d1cfaddeafb8fcf2e2862ad5f Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 13 Jun 2023 15:50:16 +0100 Subject: [PATCH 05/12] more fixes Signed-off-by: ppalmes --- examples/AutoOfflineRL/examples/script.jl | 25 ++++++----------------- src/skpreprocessor.jl | 6 +++--- 2 files changed, 9 insertions(+), 22 deletions(-) diff --git a/examples/AutoOfflineRL/examples/script.jl b/examples/AutoOfflineRL/examples/script.jl index 9d1af1fe..2f95c714 100644 --- a/examples/AutoOfflineRL/examples/script.jl +++ b/examples/AutoOfflineRL/examples/script.jl @@ -2,10 +2,6 @@ using AutoOfflineRL using AutoMLPipeline using Parquet using DataFrames -<<<<<<< Updated upstream - -======= ->>>>>>> Stashed changes # load preprocessing elements #### Scaler rb = SKPreprocessor("RobustScaler"); @@ -38,24 +34,15 @@ reward = df[:,["reward"]] |> deepcopy |> DataFrame action = df[:,["action"]] |> deepcopy |> DataFrame action_reward = DataFrame[action, reward] -rlagent = DiscreteRLOffline(Dict(:name=>"NFQ",:rlagent=>"NFQ", - :runtime_args=>Dict(:n_epochs=>10))) - - -pipeline = (numf |> ica ) + (numf |> pca) |> rlagent - -fit_transform!(pipeline,df) +rlagent = DiscreteRLOffline("DiscreteSAC",Dict( + :runtime_args=>Dict(:n_epochs=>1))) +p = ((numf |> ica ) + (numf |> pca)) |> rlagent +res = fit_transform!(p,df_input,action_reward) +[x.value[1] for x in res] |> sum -p = @pipeline ((numf |> ica ) + (numf |> pca)) |> rlagent - -dfnew=fit_transform(p,df_input) - -|> rlagent -pipeline = rlagent +AutoOfflineRL.transform!(pipeline,df_input) -fit!(p,df_input,action_reward) -AutoOfflineRL.transform!(pipeline,df_input) diff --git a/src/skpreprocessor.jl b/src/skpreprocessor.jl index 78aa3622..47c0f961 100644 --- a/src/skpreprocessor.jl +++ b/src/skpreprocessor.jl @@ -159,9 +159,9 @@ end function fit!(skp::SKPreprocessor, x::DataFrame, yc::Vector=[])::Nothing features = x |> Array y = yc - if !(eltype(yc) <: Real) - y = yc |> Vector{String} - end + #if !(eltype(yc) <: Real) + # y = yc |> Vector{String} + #end impl_args = copy(skp.model[:impl_args]) autocomp = skp.model[:autocomponent] From 89fe528f7ae7e38b1718a32b8affdae450bf9789 Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 13 Jun 2023 21:49:21 +0100 Subject: [PATCH 06/12] add parallelism Signed-off-by: ppalmes --- examples/AutoOfflineRL/examples/Project.toml | 2 +- examples/AutoOfflineRL/examples/script.jl | 98 +++++++++++++------- examples/AutoOfflineRL/src/offlinerls.jl | 4 +- 3 files changed, 65 insertions(+), 39 deletions(-) diff --git a/examples/AutoOfflineRL/examples/Project.toml b/examples/AutoOfflineRL/examples/Project.toml index 2a65d78c..91c7792c 100644 --- a/examples/AutoOfflineRL/examples/Project.toml +++ b/examples/AutoOfflineRL/examples/Project.toml @@ -2,5 +2,5 @@ AutoMLPipeline = "08437348-eef5-4817-bc1b-d4e9459680d6" AutoOfflineRL = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -MicroMamba = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea" +Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" diff --git a/examples/AutoOfflineRL/examples/script.jl b/examples/AutoOfflineRL/examples/script.jl index 2f95c714..c0be18c3 100644 --- a/examples/AutoOfflineRL/examples/script.jl +++ b/examples/AutoOfflineRL/examples/script.jl @@ -1,28 +1,34 @@ -using AutoOfflineRL -using AutoMLPipeline -using Parquet -using DataFrames -# load preprocessing elements -#### Scaler -rb = SKPreprocessor("RobustScaler"); -pt = SKPreprocessor("PowerTransformer"); -norm = SKPreprocessor("Normalizer"); -mx = SKPreprocessor("MinMaxScaler"); -std = SKPreprocessor("StandardScaler") -disc = CatNumDiscriminator(); -#### categorical preprocessing -ohe = OneHotEncoder(); -#### Column selector -catf = CatFeatureSelector(); -numf = NumFeatureSelector(); -# load filters -#### Decomposition -apca = SKPreprocessor("PCA",Dict(:autocomponent=>true)); -pca = SKPreprocessor("PCA"); -afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true)); -fa = SKPreprocessor("FactorAnalysis"); -aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true)); -ica = SKPreprocessor("FastICA"); +using Distributed + +nprocs() == 1 && addprocs() + +@everywhere begin + using AutoOfflineRL + using AutoMLPipeline + using Parquet + using DataFrames +end +@everywhere begin + # load preprocessing elements + #### Scaler + rb = SKPreprocessor("RobustScaler"); + pt = SKPreprocessor("PowerTransformer"); + norm = SKPreprocessor("Normalizer"); + mx = SKPreprocessor("MinMaxScaler"); + std = SKPreprocessor("StandardScaler") + ##### Column selector + catf = CatFeatureSelector(); + numf = NumFeatureSelector(); + ## load filters + ##### Decomposition + apca = SKPreprocessor("PCA",Dict(:autocomponent=>true)); + pca = SKPreprocessor("PCA"); + afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true)); + fa = SKPreprocessor("FactorAnalysis"); + aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true)); + ica = SKPreprocessor("FastICA"); + noop = Identity(Dict(:name => "Noop")); +end # load dataset path = pkgdir(AutoOfflineRL) @@ -34,16 +40,36 @@ reward = df[:,["reward"]] |> deepcopy |> DataFrame action = df[:,["action"]] |> deepcopy |> DataFrame action_reward = DataFrame[action, reward] -rlagent = DiscreteRLOffline("DiscreteSAC",Dict( - :runtime_args=>Dict(:n_epochs=>1))) -p = ((numf |> ica ) + (numf |> pca)) |> rlagent -res = fit_transform!(p,df_input,action_reward) -[x.value[1] for x in res] |> sum - -AutoOfflineRL.transform!(pipeline,df_input) - - - +agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] +scalers = [rb,pt,norm,std,mx,noop] +extractors = [pca,ica,fa,noop]; +dfresults = @sync @distributed (vcat) for agentname in agentnames + @distributed (vcat) for sc in scalers + @distributed (vcat) for xt in extractors + try + rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) + rlpipeline = ((numf |> sc |> xt)) |> rlagent + res = fit_transform!(rlpipeline,df_input,action_reward) + s = [x.value[1] for x in res] |> sum + scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] + pname = "$scn |> $xtn |> $lrn" + if !isnan(s) + DataFrame(pipeline=pname,perf=s) + else + DataFrame() + end + catch e + println("error in $agentname") + #DataFrame(agent=agentname,scaler=sc,xtractor=xt,perf=NaN) + DataFrame() + end + end + end +end +sort!(dfresults,:perf,rev=true) +show(dfresults,allcols=true,allrows=true,truncate=0) -a=fit_transform!(pipeline,df) +todo: +run parallel search +evaluation diff --git a/examples/AutoOfflineRL/src/offlinerls.jl b/examples/AutoOfflineRL/src/offlinerls.jl index 63f003fa..df34d336 100644 --- a/examples/AutoOfflineRL/src/offlinerls.jl +++ b/examples/AutoOfflineRL/src/offlinerls.jl @@ -69,8 +69,8 @@ mutable struct DiscreteRLOffline <: Learner ) ) cargs = nested_dict_merge(default_args,args) - datestring = Dates.format(now(), "yyyy-mm-dd-HH-MM") - cargs[:name] = cargs[:name]*"_"*datestring + #datestring = Dates.format(now(), "yyyy-mm-dd-HH-MM") + cargs[:name] = cargs[:name]*"_"*randstring(3) rlagent = cargs[:rlagent] if !(rlagent in keys(rl_dict)) println("error: $rlagent is not supported.") From 5230a3100417a04e8de13e6a4d826e31172b167b Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 13 Jun 2023 22:59:52 +0100 Subject: [PATCH 07/12] wrap pipeline search to a function Signed-off-by: ppalmes --- examples/AutoOfflineRL/examples/script.jl | 56 ++++++++++++----------- 1 file changed, 29 insertions(+), 27 deletions(-) diff --git a/examples/AutoOfflineRL/examples/script.jl b/examples/AutoOfflineRL/examples/script.jl index c0be18c3..9490a4bc 100644 --- a/examples/AutoOfflineRL/examples/script.jl +++ b/examples/AutoOfflineRL/examples/script.jl @@ -21,11 +21,11 @@ end numf = NumFeatureSelector(); ## load filters ##### Decomposition - apca = SKPreprocessor("PCA",Dict(:autocomponent=>true)); + #apca = SKPreprocessor("PCA",Dict(:autocomponent=>true,:name=>"autoPCA")); + #afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true,:name=>"autoFA")); + #aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true,:name=>"autoICA")); pca = SKPreprocessor("PCA"); - afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true)); fa = SKPreprocessor("FactorAnalysis"); - aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true)); ica = SKPreprocessor("FastICA"); noop = Identity(Dict(:name => "Noop")); end @@ -40,36 +40,38 @@ reward = df[:,["reward"]] |> deepcopy |> DataFrame action = df[:,["action"]] |> deepcopy |> DataFrame action_reward = DataFrame[action, reward] -agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] -scalers = [rb,pt,norm,std,mx,noop] -extractors = [pca,ica,fa,noop]; -dfresults = @sync @distributed (vcat) for agentname in agentnames - @distributed (vcat) for sc in scalers - @distributed (vcat) for xt in extractors - try - rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) - rlpipeline = ((numf |> sc |> xt)) |> rlagent - res = fit_transform!(rlpipeline,df_input,action_reward) - s = [x.value[1] for x in res] |> sum - scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] - pname = "$scn |> $xtn |> $lrn" - if !isnan(s) - DataFrame(pipeline=pname,perf=s) - else +function pipelinesearch() + agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] + scalers = [rb,pt,norm,std,mx,noop] + extractors = [pca,ica,fa,noop] + dfresults = @sync @distributed (vcat) for agentname in agentnames + @distributed (vcat) for sc in scalers + @distributed (vcat) for xt in extractors + try + rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) + rlpipeline = ((numf |> sc |> xt)) |> rlagent + res = fit_transform!(rlpipeline,df_input,action_reward) + s = [x.value[1] for x in res] |> sum + scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] + pname = "$scn |> $xtn |> $lrn" + if !isnan(s) + DataFrame(pipeline=pname,perf=s) + else + DataFrame() + end + catch e + println("error in $agentname") + #DataFrame(agent=agentname,scaler=sc,xtractor=xt,perf=NaN) DataFrame() end - catch e - println("error in $agentname") - #DataFrame(agent=agentname,scaler=sc,xtractor=xt,perf=NaN) - DataFrame() end end end + sort!(dfresults,:perf,rev=true) + return dfresults end -sort!(dfresults,:perf,rev=true) -show(dfresults,allcols=true,allrows=true,truncate=0) - +dftable= pipelinesearch() +show(dftable,allcols=true,allrows=true,truncate=0) todo: -run parallel search evaluation From 737290b4314d21f74633d130b2ddc9af5f56263b Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 20 Jun 2023 19:17:12 +0100 Subject: [PATCH 08/12] add crossvalidation Signed-off-by: ppalmes --- examples/AutoOfflineRL/CondaPkg.toml | 3 + examples/AutoOfflineRL/Project.toml | 1 + examples/AutoOfflineRL/examples/Project.toml | 1 + examples/AutoOfflineRL/examples/script.jl | 17 ++-- examples/AutoOfflineRL/examples/test.py | 30 ++++++ examples/AutoOfflineRL/src/AutoOfflineRL.jl | 3 +- examples/AutoOfflineRL/src/offlinerls.jl | 97 ++++++++++++++++---- examples/AutoOfflineRL/src/utils.jl | 35 ------- examples/AutoOfflineRL/test/runtests.jl | 12 ++- 9 files changed, 132 insertions(+), 67 deletions(-) create mode 100644 examples/AutoOfflineRL/examples/test.py delete mode 100644 examples/AutoOfflineRL/src/utils.jl diff --git a/examples/AutoOfflineRL/CondaPkg.toml b/examples/AutoOfflineRL/CondaPkg.toml index e3385880..13735507 100644 --- a/examples/AutoOfflineRL/CondaPkg.toml +++ b/examples/AutoOfflineRL/CondaPkg.toml @@ -1,7 +1,10 @@ +channels = ["mkl", "scikit-learn"] + [deps] scikit-learn = "" pandas = "" numpy = "" +python = "" [pip.deps] d3rlpy = "" diff --git a/examples/AutoOfflineRL/Project.toml b/examples/AutoOfflineRL/Project.toml index 8aae597e..f462f017 100644 --- a/examples/AutoOfflineRL/Project.toml +++ b/examples/AutoOfflineRL/Project.toml @@ -14,6 +14,7 @@ MicroMamba = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea" Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" [compat] julia = "1" diff --git a/examples/AutoOfflineRL/examples/Project.toml b/examples/AutoOfflineRL/examples/Project.toml index 91c7792c..1e27f477 100644 --- a/examples/AutoOfflineRL/examples/Project.toml +++ b/examples/AutoOfflineRL/examples/Project.toml @@ -4,3 +4,4 @@ AutoOfflineRL = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" diff --git a/examples/AutoOfflineRL/examples/script.jl b/examples/AutoOfflineRL/examples/script.jl index 9490a4bc..58a0d688 100644 --- a/examples/AutoOfflineRL/examples/script.jl +++ b/examples/AutoOfflineRL/examples/script.jl @@ -40,6 +40,10 @@ reward = df[:,["reward"]] |> deepcopy |> DataFrame action = df[:,["action"]] |> deepcopy |> DataFrame action_reward = DataFrame[action, reward] +agent = DiscreteRLOffline("NFQ") +pipe = (numf |> mx |> pca) |> agent +crossvalidateRL(pipe,df_input,action_reward) + function pipelinesearch() agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] scalers = [rb,pt,norm,std,mx,noop] @@ -50,12 +54,13 @@ function pipelinesearch() try rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) rlpipeline = ((numf |> sc |> xt)) |> rlagent - res = fit_transform!(rlpipeline,df_input,action_reward) - s = [x.value[1] for x in res] |> sum + #res = fit_transform!(rlpipeline,df_input,action_reward) + #s = [x.value[1] for x in res] |> sum + res = crossvalidateRL(rlpipeline,df_input,action_reward) scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] pname = "$scn |> $xtn |> $lrn" - if !isnan(s) - DataFrame(pipeline=pname,perf=s) + if !isnan(res) + DataFrame(pipeline=pname,perf=res) else DataFrame() end @@ -67,11 +72,9 @@ function pipelinesearch() end end end - sort!(dfresults,:perf,rev=true) + sort!(dfresults,:perf,rev=false) return dfresults end dftable= pipelinesearch() show(dftable,allcols=true,allrows=true,truncate=0) -todo: -evaluation diff --git a/examples/AutoOfflineRL/examples/test.py b/examples/AutoOfflineRL/examples/test.py new file mode 100644 index 00000000..5db9a34f --- /dev/null +++ b/examples/AutoOfflineRL/examples/test.py @@ -0,0 +1,30 @@ +from d3rlpy.datasets import get_cartpole +from d3rlpy.algos import DQN +from d3rlpy.metrics.scorer import td_error_scorer +from d3rlpy.metrics.scorer import average_value_estimation_scorer +from d3rlpy.metrics.scorer import evaluate_on_environment +from sklearn.model_selection import train_test_split + +dataset, env = get_cartpole() +train_episodes, test_episodes = train_test_split(dataset) +dqn = DQN() +dqn.fit(train_episodes, + eval_episodes=test_episodes, + n_epochs=1, + scorers={ + 'td_error': td_error_scorer, + 'value_scale': average_value_estimation_scorer + } + ) + + +from sklearn.model_selection import cross_validate + +scores = cross_validate(dqn, + dataset, + scoring={ + 'td_error': td_error_scorer, + 'value_scale': average_value_estimation_scorer + }, + fit_params={'n_epochs':1} + ) diff --git a/examples/AutoOfflineRL/src/AutoOfflineRL.jl b/examples/AutoOfflineRL/src/AutoOfflineRL.jl index 87aa72d8..2ef51c6f 100644 --- a/examples/AutoOfflineRL/src/AutoOfflineRL.jl +++ b/examples/AutoOfflineRL/src/AutoOfflineRL.jl @@ -45,7 +45,6 @@ include("offlinerls.jl") using .OfflineRLs export DiscreteRLOffline, fit! export driver, listdiscreateagents - - +export crossvalidateRL end # module diff --git a/examples/AutoOfflineRL/src/offlinerls.jl b/examples/AutoOfflineRL/src/offlinerls.jl index df34d336..d0a96127 100644 --- a/examples/AutoOfflineRL/src/offlinerls.jl +++ b/examples/AutoOfflineRL/src/offlinerls.jl @@ -1,6 +1,8 @@ module OfflineRLs using AutoOfflineRL +using PythonCall +import Statistics using Parquet using Distributed using DataFrames: DataFrame, dropmissing @@ -19,21 +21,34 @@ using ..Utils: nested_dict_merge export DiscreteRLOffline, fit!, transform!, fit, transform export listdiscreateagents, driver +export crossvalidateRL const rl_dict = Dict{String, PYC.Py}() +const metric_dict = Dict{String, PYC.Py}() const PYRL = PYC.pynew() const PYPD = PYC.pynew() const PYNP = PYC.pynew() const PYDT = PYC.pynew() +const PYMT = PYC.pynew() +const PYSK = PYC.pynew() + function __init__() PYC.pycopy!(PYRL, PYC.pyimport("d3rlpy.algos")) - PYC.pycopy!(PYDT, PYC.pyimport("d3rlpy.dataset")) + PYC.pycopy!(PYDT, PYC.pyimport("d3rlpy.datasets")) + PYC.pycopy!(PYSK, PYC.pyimport("sklearn.model_selection")) + PYC.pycopy!(PYMT, PYC.pyimport("d3rlpy.metrics")) PYC.pycopy!(PYPD, PYC.pyimport("pandas")) PYC.pycopy!(PYNP, PYC.pyimport("numpy")) # OfflineRLs + metric_dict["cross_validate"] = PYSK.cross_validate + metric_dict["train_test_split"] = PYSK.train_test_split + metric_dict["td_error_scorer"] = PYMT.td_error_scorer + metric_dict["average_value_estimation_scorer"] = PYMT.average_value_estimation_scorer + metric_dict["get_cartpole"] = PYDT.get_cartpole + rl_dict["DiscreteBC"] = PYRL rl_dict["DQN"] = PYRL rl_dict["NFQ"] = PYRL @@ -191,27 +206,73 @@ function transform!(agent::DiscreteRLOffline,df::DataFrame=DataFrame())::Vector return res end +function prp_fit_transform(pipe::Machine, instances::DataFrame,actreward::Vector) + machines = pipe.model[:machines] + machine_args = pipe.model[:machine_args] + + current_instances = instances + trlength = length(machines) + for t_index in 1:(trlength - 1) + machine = createmachine(machines[t_index], machine_args) + fit!(machine, current_instances, actreward) + current_instances = transform!(machine, current_instances) + end + return current_instances +end + + function driver() - #dataset = ENV["HOME"]*"/phome/ibmgithub/ZOS/data/processed_batch.csv" path = pkgdir(AutoOfflineRL) dataset = "$path/data/smalldata.parquet" df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing - #for agentid in reverse([keys(rl_dict)...]) - # println(agentid) - # if agentid != "DiscreteRandomPolicy" - # agent = DiscreteRLOffline(agentid) - # fit!(agent,df) - # end - #end - #discreteagents() - agent = DiscreteRLOffline("DoubleDQN"; tag="sac") - header = agent.model[:o_header] - fit!(agent,df) - transform!(agent,df[1:20,:]) - #vec = df[1,header] |> Vector - #transform(agent,vec) - #m = DiscreteRLOffline() - #dqn_agent(m,dat) + df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] + reward = df[:,["reward"]] |> deepcopy |> DataFrame + action = df[:,["action"]] |> deepcopy |> DataFrame + action_reward = DataFrame[action, reward] + agentname="NFQ" + agent = DiscreteRLOffline(agentname) + #fit_transform!(agent,df_input,action_reward) +end + +function crossvalidateRL(pp::Machine, dfobs::DataFrame, actreward::Vector) + pipe = deepcopy(pp) + features = deepcopy(dfobs) + machines = pipe.model[:machines] + agent = machines[end] + + runtime_args = agent.model[:runtime_args] + logging = agent.model[:save_metrics] + impl_args = copy(agent.model[:impl_args]) + rlagent = agent.model[:rlagent] + py_rlagent = getproperty(rl_dict[rlagent],rlagent) + pyrlobj = py_rlagent(;impl_args...) + + df_input = prp_fit_transform(pipe,features,actreward) + mdp_dataset = createmdpdata!(agent,df_input,actreward) + + td_error_scorer = PYMT.td_error_scorer + runconfig = Dict(:scorers=>Dict("td_error"=>td_error_scorer)) + score=pyrlobj.fit(mdp_dataset; + eval_episodes=mdp_dataset, + runtime_args...,runconfig...) + vals = pyconvert(Array,score) + mvals = [v[2]["td_error"] for v in vals] |> Statistics.mean + return mvals + #return mdp_dataset + #py_train_test_split = metric_dict["train_test_split"] + + #py_get_cartpole = metric_dict["get_cartpole"] + #dataset,_ = py_get_cartpole() + #train_episodes, test_episodes = py_train_test_split([mdp_dataset,mdp_dataset,mdp_dataset]) + #py_train_episodes,py_test_episodes = py_train_test_split(mdp_dataset; test_size=0.25) + + #pycrossvalidate = getproperty(metric_dict["cross_validate"],"cross_validate") + #td_error_scorer = PYMT.td_error_scorer + #runconfig = Dict(:scoring=>Dict(:td_error=>td_error_scorer), + # :fit_params=>Dict(:n_epochs=>1,:n_samples=>1,:n_splits=>1)) + #scores = pycrossvalidate(pyrlobj,mdp_dataset; runconfig...) + ##pyrlobj.fit(mdp_dataset; save_metrics = logging, runtime_args... ) + #return scores end diff --git a/examples/AutoOfflineRL/src/utils.jl b/examples/AutoOfflineRL/src/utils.jl deleted file mode 100644 index 886fcf12..00000000 --- a/examples/AutoOfflineRL/src/utils.jl +++ /dev/null @@ -1,35 +0,0 @@ -module Utils - -export nested_dict_merge - -""" - nested_dict_merge(first::Dict, second::Dict) - -Second nested dictionary is merged into first. - -If a second dictionary's value as well as the first -are both dictionaries, then a merge is conducted between -the two inner dictionaries. -Otherwise the second's value overrides the first. - -- `first`: first nested dictionary -- `second`: second nested dictionary - -Returns: merged nested dictionary -""" -function nested_dict_merge(first::Dict, second::Dict) - target = copy(first) - for (second_key, second_value) in second - values_both_dict = - typeof(second_value) <: Dict && - typeof(get(target, second_key, nothing)) <: Dict - if values_both_dict - target[second_key] = nested_dict_merge(target[second_key], second_value) - else - target[second_key] = second_value - end - end - return target -end - -end diff --git a/examples/AutoOfflineRL/test/runtests.jl b/examples/AutoOfflineRL/test/runtests.jl index d5733ef5..97e2c086 100644 --- a/examples/AutoOfflineRL/test/runtests.jl +++ b/examples/AutoOfflineRL/test/runtests.jl @@ -37,14 +37,18 @@ end path = pkgdir(AutoOfflineRL) dataset = "$path/data/smalldata.parquet" df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing + df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] + reward = df[:,["reward"]] |> deepcopy |> DataFrame + action = df[:,["action"]] |> deepcopy |> DataFrame + action_reward = DataFrame[action, reward] for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) @info "training $agentid" - agent = DiscreteRLOffline(agentid; save_model=false) + agent = DiscreteRLOffline(agentid; save_model=false,runtime_args=Dict(:n_epochs=>1,:verbose=>false, :show_progress=>true)) o_header = agent.model[:o_header] - fit!(agent,df; n_epochs=1,verbose=false, show_progress=true) + fit!(agent,df_input,action_reward) @test agent.model[:rlobjtrained] !== PYC.PyNULL @info "transform $agentid" - adf = df[1:2,:] + adf = df_input[1:2,:] if agentid != "DiscreteBC" res = AutoOfflineRL.transform!(agent,adf) @test typeof(res[1]) .== NamedTuple{(:obs,:action, :value), Tuple{Vector{Float64},Vector{Float64}, Vector{Float64}}} @@ -53,6 +57,4 @@ end end - - end From 524380947bd9b08ea31833b8fbb4130ecaaa89c1 Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 20 Jun 2023 20:01:58 +0100 Subject: [PATCH 09/12] move up directory Signed-off-by: ppalmes --- AutoOfflineRL/CondaPkg.toml | 10 + AutoOfflineRL/LICENSE | 21 ++ AutoOfflineRL/Project.toml | 26 +++ AutoOfflineRL/README.md | 0 AutoOfflineRL/data/smalldata.parquet | Bin 0 -> 215046 bytes AutoOfflineRL/examples/Project.toml | 7 + AutoOfflineRL/examples/script.jl | 77 ++++++++ AutoOfflineRL/examples/test.py | 30 +++ AutoOfflineRL/src/AutoOfflineRL.jl | 50 +++++ AutoOfflineRL/src/offlinerls.jl | 279 +++++++++++++++++++++++++++ AutoOfflineRL/test/runtests.jl | 60 ++++++ 11 files changed, 560 insertions(+) create mode 100644 AutoOfflineRL/CondaPkg.toml create mode 100644 AutoOfflineRL/LICENSE create mode 100644 AutoOfflineRL/Project.toml create mode 100644 AutoOfflineRL/README.md create mode 100644 AutoOfflineRL/data/smalldata.parquet create mode 100644 AutoOfflineRL/examples/Project.toml create mode 100644 AutoOfflineRL/examples/script.jl create mode 100644 AutoOfflineRL/examples/test.py create mode 100644 AutoOfflineRL/src/AutoOfflineRL.jl create mode 100644 AutoOfflineRL/src/offlinerls.jl create mode 100644 AutoOfflineRL/test/runtests.jl diff --git a/AutoOfflineRL/CondaPkg.toml b/AutoOfflineRL/CondaPkg.toml new file mode 100644 index 00000000..13735507 --- /dev/null +++ b/AutoOfflineRL/CondaPkg.toml @@ -0,0 +1,10 @@ +channels = ["mkl", "scikit-learn"] + +[deps] +scikit-learn = "" +pandas = "" +numpy = "" +python = "" + +[pip.deps] +d3rlpy = "" diff --git a/AutoOfflineRL/LICENSE b/AutoOfflineRL/LICENSE new file mode 100644 index 00000000..554ba210 --- /dev/null +++ b/AutoOfflineRL/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Paulito Palmes, PhD and contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/AutoOfflineRL/Project.toml b/AutoOfflineRL/Project.toml new file mode 100644 index 00000000..f462f017 --- /dev/null +++ b/AutoOfflineRL/Project.toml @@ -0,0 +1,26 @@ +name = "AutoOfflineRL" +uuid = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" +authors = ["Paulito Palmes, PhD "] +version = "0.1.0" + +[deps] +AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6" +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" +Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" +MicroMamba = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea" +Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" + +[compat] +julia = "1" + +[extras] +Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" + +[targets] +test = ["Test"] diff --git a/AutoOfflineRL/README.md b/AutoOfflineRL/README.md new file mode 100644 index 00000000..e69de29b diff --git a/AutoOfflineRL/data/smalldata.parquet b/AutoOfflineRL/data/smalldata.parquet new file mode 100644 index 0000000000000000000000000000000000000000..f244e6f7446b53345e8c370e66e28c92fffb95bb GIT binary patch literal 215046 zcmeFZdzf2gx%Yn;3asfgZKlW7X?r(4r-wx|EiLJRl9sjv%4#z$g>VkXra&OWF`QEl z5`lpXC@uem_sO_u-Qd7fJ6QJ#Vy# z@X@GI(lP1-OAh{bPyz=fa8LpVC2&vz2PJS&0tY29X$Q^;ed6yNEXQe3-$FNc%fA0P9- zCcVDDCB5>&sQ>T(dw-PZhR*+TjeqA4e{QU*J3Zy^eB1x>c4jVzc83lWxs0=%Q3nJ+ zV&rnzxMMm|;r~)@f9DUeLOJ}vn$8gi)^w}`YdReV)^x@kSkvh|u%7uLJ0^S}q*Tm7Ms*)ZN!0<9$ow6XD)e$BLmwT#{OT+IQ;L4q`UQVj 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zHw&K(BY}yJm&wK2!@|Xio1P~whK?tHa2-$8;5uSe0Xu=4p6`_=ldGkzwSxsW7lE5m zl%AKt%EFVI@b@*Nt&_XUuLhIDB}aER>t9UB5 zwV5m|-7Yyft~Ob)3%@!{AguP7kDrfBEI!Q7&G^6L`A-l39!xF{CigILErCG%?)f|7 z?|%OdxvNC{co<_+%ISUw{Tk2ju*qY@5-$vk`tgsj|BUl@4F1|^IUNS2r!oEhuK&9M ze+RDKd{B@&$cau~{Py9Nz5d(vww($Qy<_v)+DQwH?b0Mx>y&{OQ e>sh!syIZ?S*x3t7O2|q`5)A1H1id4t_x}&#UH#So literal 0 HcmV?d00001 diff --git a/AutoOfflineRL/examples/Project.toml b/AutoOfflineRL/examples/Project.toml new file mode 100644 index 00000000..1e27f477 --- /dev/null +++ b/AutoOfflineRL/examples/Project.toml @@ -0,0 +1,7 @@ +[deps] +AutoMLPipeline = "08437348-eef5-4817-bc1b-d4e9459680d6" +AutoOfflineRL = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" +Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" diff --git a/AutoOfflineRL/examples/script.jl b/AutoOfflineRL/examples/script.jl new file mode 100644 index 00000000..0be867b7 --- /dev/null +++ b/AutoOfflineRL/examples/script.jl @@ -0,0 +1,77 @@ +using Distributed + +nprocs() == 1 && addprocs() + +@everywhere begin + using AutoOfflineRL + using AutoMLPipeline + using Parquet + using DataFrames +end +@everywhere begin + # load preprocessing elements + #### Scaler + rb = SKPreprocessor("RobustScaler"); + pt = SKPreprocessor("PowerTransformer"); + norm = SKPreprocessor("Normalizer"); + mx = SKPreprocessor("MinMaxScaler"); + std = SKPreprocessor("StandardScaler") + ##### Column selector + catf = CatFeatureSelector(); + numf = NumFeatureSelector(); + ## load filters + ##### Decomposition + #apca = SKPreprocessor("PCA",Dict(:autocomponent=>true,:name=>"autoPCA")); + #afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true,:name=>"autoFA")); + #aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true,:name=>"autoICA")); + pca = SKPreprocessor("PCA"); + fa = SKPreprocessor("FactorAnalysis"); + ica = SKPreprocessor("FastICA"); + noop = Identity(Dict(:name => "Noop")); +end + +# load dataset +path = pkgdir(AutoOfflineRL) +dataset = "$path/data/smalldata.parquet" +df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing + +df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] +reward = df[:,["reward"]] |> deepcopy |> DataFrame +action = df[:,["action"]] |> deepcopy |> DataFrame +action_reward = DataFrame[action, reward] + +agent = DiscreteRLOffline("NFQ") +pipe = (numf |> mx |> pca) |> agent +crossvalidateRL(pipe,df_input,action_reward) + +function pipelinesearch() + agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] + scalers = [rb,pt,norm,std,mx,noop] + extractors = [pca,ica,fa,noop] + dfresults = @sync @distributed (vcat) for agentname in agentnames + @distributed (vcat) for sc in scalers + @distributed (vcat) for xt in extractors + try + rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) + rlpipeline = ((numf |> sc |> xt)) |> rlagent + res = crossvalidateRL(rlpipeline,df_input,action_reward) + scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] + pname = "$scn |> $xtn |> $lrn" + if !isnan(res) + DataFrame(pipeline=pname,perf=res) + else + DataFrame() + end + catch e + println("error in $agentname") + DataFrame() + end + end + end + end + sort!(dfresults,:perf,rev=false) + return dfresults +end +dftable= pipelinesearch() +show(dftable,allcols=true,allrows=true,truncate=0) + diff --git a/AutoOfflineRL/examples/test.py b/AutoOfflineRL/examples/test.py new file mode 100644 index 00000000..5db9a34f --- /dev/null +++ b/AutoOfflineRL/examples/test.py @@ -0,0 +1,30 @@ +from d3rlpy.datasets import get_cartpole +from d3rlpy.algos import DQN +from d3rlpy.metrics.scorer import td_error_scorer +from d3rlpy.metrics.scorer import average_value_estimation_scorer +from d3rlpy.metrics.scorer import evaluate_on_environment +from sklearn.model_selection import train_test_split + +dataset, env = get_cartpole() +train_episodes, test_episodes = train_test_split(dataset) +dqn = DQN() +dqn.fit(train_episodes, + eval_episodes=test_episodes, + n_epochs=1, + scorers={ + 'td_error': td_error_scorer, + 'value_scale': average_value_estimation_scorer + } + ) + + +from sklearn.model_selection import cross_validate + +scores = cross_validate(dqn, + dataset, + scoring={ + 'td_error': td_error_scorer, + 'value_scale': average_value_estimation_scorer + }, + fit_params={'n_epochs':1} + ) diff --git a/AutoOfflineRL/src/AutoOfflineRL.jl b/AutoOfflineRL/src/AutoOfflineRL.jl new file mode 100644 index 00000000..2ef51c6f --- /dev/null +++ b/AutoOfflineRL/src/AutoOfflineRL.jl @@ -0,0 +1,50 @@ +module AutoOfflineRL + +using DataFrames +using CSV + +export fit, fit!, transform, transform!,fit_transform, fit_transform! +import AMLPipelineBase.AbsTypes: fit, fit!, transform, transform! + +using AMLPipelineBase +using AMLPipelineBase: AbsTypes, Utils, BaselineModels, Pipelines +using AMLPipelineBase: BaseFilters, FeatureSelectors, DecisionTreeLearners +using AMLPipelineBase: EnsembleMethods, CrossValidators +using AMLPipelineBase: NARemovers + +export Machine, Learner, Transformer, Workflow, Computer +export holdout, kfold, score, infer_eltype, nested_dict_to_tuples, + nested_dict_set!, nested_dict_merge, create_transformer, + mergedict, getiris,getprofb, + skipmean,skipmedian,skipstd, + aggregatorclskipmissing +export Baseline, Identity +export Imputer,OneHotEncoder,Wrapper +export PrunedTree,RandomForest,Adaboost +export VoteEnsemble, StackEnsemble, BestLearner +export FeatureSelector, CatFeatureSelector, NumFeatureSelector, CatNumDiscriminator +export crossvalidate +export NARemover +export @pipeline +export @pipelinex +export @pipelinez +export +, |>, *, |, >> +export Pipeline, ComboPipeline + + + +#export RLMachine, RLOffline, WeeklyEpisodes + + +#Base.@kwdef struct WeeklyEpisodes <: RLMachine +# _params::Dict = Dict() +# _model::Dict = Dict() +#end + +include("offlinerls.jl") +using .OfflineRLs +export DiscreteRLOffline, fit! +export driver, listdiscreateagents +export crossvalidateRL + +end # module diff --git a/AutoOfflineRL/src/offlinerls.jl b/AutoOfflineRL/src/offlinerls.jl new file mode 100644 index 00000000..d0a96127 --- /dev/null +++ b/AutoOfflineRL/src/offlinerls.jl @@ -0,0 +1,279 @@ +module OfflineRLs + +using AutoOfflineRL +using PythonCall +import Statistics +using Parquet +using Distributed +using DataFrames: DataFrame, dropmissing +using Random +using CSV +using Dates + +using ..AbsTypes +using ..Utils +import ..AbsTypes: fit, fit!, transform, transform! + +import PythonCall +const PYC = PythonCall + +using ..Utils: nested_dict_merge + +export DiscreteRLOffline, fit!, transform!, fit, transform +export listdiscreateagents, driver +export crossvalidateRL + +const rl_dict = Dict{String, PYC.Py}() +const metric_dict = Dict{String, PYC.Py}() + +const PYRL = PYC.pynew() +const PYPD = PYC.pynew() +const PYNP = PYC.pynew() +const PYDT = PYC.pynew() +const PYMT = PYC.pynew() +const PYSK = PYC.pynew() + + +function __init__() + PYC.pycopy!(PYRL, PYC.pyimport("d3rlpy.algos")) + PYC.pycopy!(PYDT, PYC.pyimport("d3rlpy.datasets")) + PYC.pycopy!(PYSK, PYC.pyimport("sklearn.model_selection")) + PYC.pycopy!(PYMT, PYC.pyimport("d3rlpy.metrics")) + PYC.pycopy!(PYPD, PYC.pyimport("pandas")) + PYC.pycopy!(PYNP, PYC.pyimport("numpy")) + + # OfflineRLs + metric_dict["cross_validate"] = PYSK.cross_validate + metric_dict["train_test_split"] = PYSK.train_test_split + metric_dict["td_error_scorer"] = PYMT.td_error_scorer + metric_dict["average_value_estimation_scorer"] = PYMT.average_value_estimation_scorer + metric_dict["get_cartpole"] = PYDT.get_cartpole + + rl_dict["DiscreteBC"] = PYRL + rl_dict["DQN"] = PYRL + rl_dict["NFQ"] = PYRL + rl_dict["DoubleDQN"] = PYRL + rl_dict["DiscreteBCQ"] = PYRL + rl_dict["DiscreteCQL"] = PYRL + rl_dict["DiscreteSAC"] = PYRL + #rl_dict["DiscreteRandomPolicy"] = PYRL +end + +mutable struct DiscreteRLOffline <: Learner + name::String + model::Dict{Symbol,Any} + + function DiscreteRLOffline(args=Dict{Symbol,Any}()) + default_args = Dict{Symbol,Any}( + :name => "DQN", + :tag => "RLOffline", + :rlagent => "DQN", + :iterations => 100, + :save_metrics => false, + :rlobjtrained => PYC.PyNULL, + :o_header => ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"], + :a_header => ["action"], + :r_header => ["reward"], + :save_model => false, + :runtime_args => Dict{Symbol, Any}( + :n_epochs => 5, + ), + :impl_args => Dict{Symbol,Any}( + :scaler => "min_max", + :use_gpu => false, + ) + ) + cargs = nested_dict_merge(default_args,args) + #datestring = Dates.format(now(), "yyyy-mm-dd-HH-MM") + cargs[:name] = cargs[:name]*"_"*randstring(3) + rlagent = cargs[:rlagent] + if !(rlagent in keys(rl_dict)) + println("error: $rlagent is not supported.") + println() + discreteagents() + error("Argument keyword error") + end + new(cargs[:name],cargs) + end +end + +function DiscreteRLOffline(rlagent::String, args::Dict) + DiscreteRLOffline(Dict(:rlagent => rlagent, :name => rlagent, args...)) +end + +function DiscreteRLOffline(rlagent::String; args...) + DiscreteRLOffline(Dict( + :rlagent => rlagent, + :name => rlagent, + args... + )) +end + +function listdiscreateagents() + println() + println("RL Discrete Agents:") + agents = keys(rl_dict) |> collect + [println(" ",agent," ") for agent in agents] + println("See d3rlpy python package for details about the agent arguments.") + nothing +end + +function discreteagents() + println() + println("syntax: DiscreteRLOffline(name::String, args::Dict)") + println("and 'args' are the agent's parameters") + println("See d3rlpy python package for details about the agent arguments.") + println("use: listdiscreateagents() to get the available RL agents") +end + +function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector) + _observations = df |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _actions = action_reward[1] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _rewards = action_reward[2] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + ## inject end of data by terminal column + nrow, _ = size(df) + _terminals = zeros(Int, nrow) + _terminals[end] = 1 + _terminals = _terminals .|> PYC.float |> x -> PYNP.array(x, dtype = "int32") + ## create dataset for RLOffline + mdp_dataset = PYDT.MDPDataset( + observations = _observations, + actions = _actions, + rewards = _rewards, + terminals = _terminals, + ) + ## save params + agent.model[:mdp_dataset] = mdp_dataset + agent.model[:np_observations] = _observations + agent.model[:np_actions] = _actions + agent.model[:np_rewards] = _rewards + return mdp_dataset +end + +function checkheaders(agent::DiscreteRLOffline, df) + o_header = agent.model[:o_header] + a_header = agent.model[:a_header] + r_header = agent.model[:r_header] + dfnames = names(df) + [@assert header in dfnames "\"$header\" is not in data header" + for header in vcat(o_header, a_header, r_header)] +end + +function fit!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector)::Nothing + # check if headers exist + #checkheaders(agent::DiscreteRLOffline, df) + # create mdp data + nrow, ncol = size(df) + mdp_dataset = createmdpdata!(agent, df,action_reward) + ## prepare algorithm + runtime_args = agent.model[:runtime_args] + logging = agent.model[:save_metrics] + impl_args = copy(agent.model[:impl_args]) + rlagent = agent.model[:rlagent] + py_rlagent = getproperty(rl_dict[rlagent],rlagent) + pyrlobj = py_rlagent(;impl_args...) + pyrlobj.fit(mdp_dataset; save_metrics = logging, runtime_args... ) + ## save rl to model dictionary + agent.model[:rlobjtrained] = pyrlobj + agent.model[:nrow] = nrow + agent.model[:ncol] = ncol + ## save model to file + if agent.model[:save_model] == true + path = pkgdir(AutoOfflineRL) + agentname = agent.model[:name] + tag = agent.model[:tag] + fnmodel = "$path/model/$(agentname)_$(tag)_model.pt" + fnpolicy = "$path/model/$(agentname)_$(tag)_policy.pt" + pyrlobj.save_model(fnmodel) + pyrlobj.save_policy(fnpolicy) + end + return nothing +end + + +function transform!(agent::DiscreteRLOffline,df::DataFrame=DataFrame())::Vector + pyrlobj = agent.model[:rlobjtrained] + #o_header = agent.model[:o_header] + observations = df |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + res = map(observations) do obs + action = pyrlobj.predict(obs) + value = pyrlobj.predict_value([obs],action) + action = PYC.pyconvert.(Float64,action) + value = PYC.pyconvert.(Float64,value) + obs = PYC.pyconvert.(Float64,obs) + (;obs,action,value) + end + return res +end + +function prp_fit_transform(pipe::Machine, instances::DataFrame,actreward::Vector) + machines = pipe.model[:machines] + machine_args = pipe.model[:machine_args] + + current_instances = instances + trlength = length(machines) + for t_index in 1:(trlength - 1) + machine = createmachine(machines[t_index], machine_args) + fit!(machine, current_instances, actreward) + current_instances = transform!(machine, current_instances) + end + return current_instances +end + + +function driver() + path = pkgdir(AutoOfflineRL) + dataset = "$path/data/smalldata.parquet" + df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing + df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] + reward = df[:,["reward"]] |> deepcopy |> DataFrame + action = df[:,["action"]] |> deepcopy |> DataFrame + action_reward = DataFrame[action, reward] + agentname="NFQ" + agent = DiscreteRLOffline(agentname) + #fit_transform!(agent,df_input,action_reward) +end + +function crossvalidateRL(pp::Machine, dfobs::DataFrame, actreward::Vector) + pipe = deepcopy(pp) + features = deepcopy(dfobs) + machines = pipe.model[:machines] + agent = machines[end] + + runtime_args = agent.model[:runtime_args] + logging = agent.model[:save_metrics] + impl_args = copy(agent.model[:impl_args]) + rlagent = agent.model[:rlagent] + py_rlagent = getproperty(rl_dict[rlagent],rlagent) + pyrlobj = py_rlagent(;impl_args...) + + df_input = prp_fit_transform(pipe,features,actreward) + mdp_dataset = createmdpdata!(agent,df_input,actreward) + + td_error_scorer = PYMT.td_error_scorer + runconfig = Dict(:scorers=>Dict("td_error"=>td_error_scorer)) + score=pyrlobj.fit(mdp_dataset; + eval_episodes=mdp_dataset, + runtime_args...,runconfig...) + vals = pyconvert(Array,score) + mvals = [v[2]["td_error"] for v in vals] |> Statistics.mean + return mvals + #return mdp_dataset + #py_train_test_split = metric_dict["train_test_split"] + + #py_get_cartpole = metric_dict["get_cartpole"] + #dataset,_ = py_get_cartpole() + #train_episodes, test_episodes = py_train_test_split([mdp_dataset,mdp_dataset,mdp_dataset]) + #py_train_episodes,py_test_episodes = py_train_test_split(mdp_dataset; test_size=0.25) + + #pycrossvalidate = getproperty(metric_dict["cross_validate"],"cross_validate") + #td_error_scorer = PYMT.td_error_scorer + #runconfig = Dict(:scoring=>Dict(:td_error=>td_error_scorer), + # :fit_params=>Dict(:n_epochs=>1,:n_samples=>1,:n_splits=>1)) + #scores = pycrossvalidate(pyrlobj,mdp_dataset; runconfig...) + ##pyrlobj.fit(mdp_dataset; save_metrics = logging, runtime_args... ) + #return scores +end + + +end diff --git a/AutoOfflineRL/test/runtests.jl b/AutoOfflineRL/test/runtests.jl new file mode 100644 index 00000000..97e2c086 --- /dev/null +++ b/AutoOfflineRL/test/runtests.jl @@ -0,0 +1,60 @@ +module TestOfflineRL +using AutoOfflineRL +using Test +using DataFrames +using PythonCall +using Parquet +const PYC=PythonCall + +@testset "Load Agents with Default Params" begin + for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) + @info "loading $agentid default params" + rlagent = DiscreteRLOffline(agentid) + @test typeof(rlagent) <: AutoOfflineRL.Learner + end +end + + +@testset "Load Agents with Param Args" begin + println() + for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) + @info "loading $agentid with customized params" + rlagent = DiscreteRLOffline(agentid, + Dict(:name=>agentid, + :iterations=>10000, + :epochs=>100) + ) + @test typeof(rlagent) <: AutoOfflineRL.Learner + end +end + +@testset "Test Exceptions" begin + @test_throws ErrorException DiscreteRLOffline("dummy") +end + +@testset "Test Agent fit!/transform Runs" begin + println() + path = pkgdir(AutoOfflineRL) + dataset = "$path/data/smalldata.parquet" + df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing + df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] + reward = df[:,["reward"]] |> deepcopy |> DataFrame + action = df[:,["action"]] |> deepcopy |> DataFrame + action_reward = DataFrame[action, reward] + for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) + @info "training $agentid" + agent = DiscreteRLOffline(agentid; save_model=false,runtime_args=Dict(:n_epochs=>1,:verbose=>false, :show_progress=>true)) + o_header = agent.model[:o_header] + fit!(agent,df_input,action_reward) + @test agent.model[:rlobjtrained] !== PYC.PyNULL + @info "transform $agentid" + adf = df_input[1:2,:] + if agentid != "DiscreteBC" + res = AutoOfflineRL.transform!(agent,adf) + @test typeof(res[1]) .== NamedTuple{(:obs,:action, :value), Tuple{Vector{Float64},Vector{Float64}, Vector{Float64}}} + end + end +end + + +end From cdebeddd22043e5a95b3aa6c791866dc0980a8ca Mon Sep 17 00:00:00 2001 From: ppalmes Date: Tue, 20 Jun 2023 20:02:39 +0100 Subject: [PATCH 10/12] move to upper directory --- examples/AutoOfflineRL/CondaPkg.toml | 10 - examples/AutoOfflineRL/LICENSE | 21 -- examples/AutoOfflineRL/Project.toml | 26 -- examples/AutoOfflineRL/README.md | 0 examples/AutoOfflineRL/data/smalldata.parquet | Bin 215046 -> 0 bytes examples/AutoOfflineRL/examples/Project.toml | 7 - examples/AutoOfflineRL/examples/script.jl | 80 ----- examples/AutoOfflineRL/examples/test.py | 30 -- examples/AutoOfflineRL/src/AutoOfflineRL.jl | 50 ---- examples/AutoOfflineRL/src/offlinerls.jl | 279 ------------------ examples/AutoOfflineRL/test/runtests.jl | 60 ---- 11 files changed, 563 deletions(-) delete mode 100644 examples/AutoOfflineRL/CondaPkg.toml delete mode 100644 examples/AutoOfflineRL/LICENSE delete mode 100644 examples/AutoOfflineRL/Project.toml delete mode 100644 examples/AutoOfflineRL/README.md delete mode 100644 examples/AutoOfflineRL/data/smalldata.parquet delete mode 100644 examples/AutoOfflineRL/examples/Project.toml delete mode 100644 examples/AutoOfflineRL/examples/script.jl delete mode 100644 examples/AutoOfflineRL/examples/test.py delete mode 100644 examples/AutoOfflineRL/src/AutoOfflineRL.jl delete mode 100644 examples/AutoOfflineRL/src/offlinerls.jl delete mode 100644 examples/AutoOfflineRL/test/runtests.jl diff --git a/examples/AutoOfflineRL/CondaPkg.toml b/examples/AutoOfflineRL/CondaPkg.toml deleted file mode 100644 index 13735507..00000000 --- a/examples/AutoOfflineRL/CondaPkg.toml +++ /dev/null @@ -1,10 +0,0 @@ -channels = ["mkl", "scikit-learn"] - -[deps] -scikit-learn = "" -pandas = "" -numpy = "" -python = "" - -[pip.deps] -d3rlpy = "" diff --git a/examples/AutoOfflineRL/LICENSE b/examples/AutoOfflineRL/LICENSE deleted file mode 100644 index 554ba210..00000000 --- a/examples/AutoOfflineRL/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 Paulito Palmes, PhD and contributors - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/examples/AutoOfflineRL/Project.toml b/examples/AutoOfflineRL/Project.toml deleted file mode 100644 index f462f017..00000000 --- a/examples/AutoOfflineRL/Project.toml +++ /dev/null @@ -1,26 +0,0 @@ -name = "AutoOfflineRL" -uuid = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" -authors = ["Paulito Palmes, PhD "] -version = "0.1.0" - -[deps] -AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6" -CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" -Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" -MicroMamba = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea" -Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" -PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" -Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" -Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" - -[compat] -julia = "1" - -[extras] -Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" - -[targets] -test = ["Test"] diff --git a/examples/AutoOfflineRL/README.md b/examples/AutoOfflineRL/README.md deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/AutoOfflineRL/data/smalldata.parquet b/examples/AutoOfflineRL/data/smalldata.parquet deleted file mode 100644 index f244e6f7446b53345e8c370e66e28c92fffb95bb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 215046 zcmeFZdzf2gx%Yn;3asfgZKlW7X?r(4r-wx|EiLJRl9sjv%4#z$g>VkXra&OWF`QEl z5`lpXC@uem_sO_u-Qd7fJ6QJ#Vy# z@X@GI(lP1-OAh{bPyz=fa8LpVC2&vz2PJS&0tY29X$Q^;ed6yNEXQe3-$FNc%fA0P9- zCcVDDCB5>&sQ>T(dw-PZhR*+TjeqA4e{QU*J3Zy^eB1x>c4jVzc83lWxs0=%Q3nJ+ 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zHw&K(BY}yJm&wK2!@|Xio1P~whK?tHa2-$8;5uSe0Xu=4p6`_=ldGkzwSxsW7lE5m zl%AKt%EFVI@b@*Nt&_XUuLhIDB}aER>t9UB5 zwV5m|-7Yyft~Ob)3%@!{AguP7kDrfBEI!Q7&G^6L`A-l39!xF{CigILErCG%?)f|7 z?|%OdxvNC{co<_+%ISUw{Tk2ju*qY@5-$vk`tgsj|BUl@4F1|^IUNS2r!oEhuK&9M ze+RDKd{B@&$cau~{Py9Nz5d(vww($Qy<_v)+DQwH?b0Mx>y&{OQ e>sh!syIZ?S*x3t7O2|q`5)A1H1id4t_x}&#UH#So diff --git a/examples/AutoOfflineRL/examples/Project.toml b/examples/AutoOfflineRL/examples/Project.toml deleted file mode 100644 index 1e27f477..00000000 --- a/examples/AutoOfflineRL/examples/Project.toml +++ /dev/null @@ -1,7 +0,0 @@ -[deps] -AutoMLPipeline = "08437348-eef5-4817-bc1b-d4e9459680d6" -AutoOfflineRL = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" -Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" -PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" diff --git a/examples/AutoOfflineRL/examples/script.jl b/examples/AutoOfflineRL/examples/script.jl deleted file mode 100644 index 58a0d688..00000000 --- a/examples/AutoOfflineRL/examples/script.jl +++ /dev/null @@ -1,80 +0,0 @@ -using Distributed - -nprocs() == 1 && addprocs() - -@everywhere begin - using AutoOfflineRL - using AutoMLPipeline - using Parquet - using DataFrames -end -@everywhere begin - # load preprocessing elements - #### Scaler - rb = SKPreprocessor("RobustScaler"); - pt = SKPreprocessor("PowerTransformer"); - norm = SKPreprocessor("Normalizer"); - mx = SKPreprocessor("MinMaxScaler"); - std = SKPreprocessor("StandardScaler") - ##### Column selector - catf = CatFeatureSelector(); - numf = NumFeatureSelector(); - ## load filters - ##### Decomposition - #apca = SKPreprocessor("PCA",Dict(:autocomponent=>true,:name=>"autoPCA")); - #afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true,:name=>"autoFA")); - #aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true,:name=>"autoICA")); - pca = SKPreprocessor("PCA"); - fa = SKPreprocessor("FactorAnalysis"); - ica = SKPreprocessor("FastICA"); - noop = Identity(Dict(:name => "Noop")); -end - -# load dataset -path = pkgdir(AutoOfflineRL) -dataset = "$path/data/smalldata.parquet" -df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing - -df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] -reward = df[:,["reward"]] |> deepcopy |> DataFrame -action = df[:,["action"]] |> deepcopy |> DataFrame -action_reward = DataFrame[action, reward] - -agent = DiscreteRLOffline("NFQ") -pipe = (numf |> mx |> pca) |> agent -crossvalidateRL(pipe,df_input,action_reward) - -function pipelinesearch() - agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] - scalers = [rb,pt,norm,std,mx,noop] - extractors = [pca,ica,fa,noop] - dfresults = @sync @distributed (vcat) for agentname in agentnames - @distributed (vcat) for sc in scalers - @distributed (vcat) for xt in extractors - try - rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) - rlpipeline = ((numf |> sc |> xt)) |> rlagent - #res = fit_transform!(rlpipeline,df_input,action_reward) - #s = [x.value[1] for x in res] |> sum - res = crossvalidateRL(rlpipeline,df_input,action_reward) - scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] - pname = "$scn |> $xtn |> $lrn" - if !isnan(res) - DataFrame(pipeline=pname,perf=res) - else - DataFrame() - end - catch e - println("error in $agentname") - #DataFrame(agent=agentname,scaler=sc,xtractor=xt,perf=NaN) - DataFrame() - end - end - end - end - sort!(dfresults,:perf,rev=false) - return dfresults -end -dftable= pipelinesearch() -show(dftable,allcols=true,allrows=true,truncate=0) - diff --git a/examples/AutoOfflineRL/examples/test.py b/examples/AutoOfflineRL/examples/test.py deleted file mode 100644 index 5db9a34f..00000000 --- a/examples/AutoOfflineRL/examples/test.py +++ /dev/null @@ -1,30 +0,0 @@ -from d3rlpy.datasets import get_cartpole -from d3rlpy.algos import DQN -from d3rlpy.metrics.scorer import td_error_scorer -from d3rlpy.metrics.scorer import average_value_estimation_scorer -from d3rlpy.metrics.scorer import evaluate_on_environment -from sklearn.model_selection import train_test_split - -dataset, env = get_cartpole() -train_episodes, test_episodes = train_test_split(dataset) -dqn = DQN() -dqn.fit(train_episodes, - eval_episodes=test_episodes, - n_epochs=1, - scorers={ - 'td_error': td_error_scorer, - 'value_scale': average_value_estimation_scorer - } - ) - - -from sklearn.model_selection import cross_validate - -scores = cross_validate(dqn, - dataset, - scoring={ - 'td_error': td_error_scorer, - 'value_scale': average_value_estimation_scorer - }, - fit_params={'n_epochs':1} - ) diff --git a/examples/AutoOfflineRL/src/AutoOfflineRL.jl b/examples/AutoOfflineRL/src/AutoOfflineRL.jl deleted file mode 100644 index 2ef51c6f..00000000 --- a/examples/AutoOfflineRL/src/AutoOfflineRL.jl +++ /dev/null @@ -1,50 +0,0 @@ -module AutoOfflineRL - -using DataFrames -using CSV - -export fit, fit!, transform, transform!,fit_transform, fit_transform! -import AMLPipelineBase.AbsTypes: fit, fit!, transform, transform! - -using AMLPipelineBase -using AMLPipelineBase: AbsTypes, Utils, BaselineModels, Pipelines -using AMLPipelineBase: BaseFilters, FeatureSelectors, DecisionTreeLearners -using AMLPipelineBase: EnsembleMethods, CrossValidators -using AMLPipelineBase: NARemovers - -export Machine, Learner, Transformer, Workflow, Computer -export holdout, kfold, score, infer_eltype, nested_dict_to_tuples, - nested_dict_set!, nested_dict_merge, create_transformer, - mergedict, getiris,getprofb, - skipmean,skipmedian,skipstd, - aggregatorclskipmissing -export Baseline, Identity -export Imputer,OneHotEncoder,Wrapper -export PrunedTree,RandomForest,Adaboost -export VoteEnsemble, StackEnsemble, BestLearner -export FeatureSelector, CatFeatureSelector, NumFeatureSelector, CatNumDiscriminator -export crossvalidate -export NARemover -export @pipeline -export @pipelinex -export @pipelinez -export +, |>, *, |, >> -export Pipeline, ComboPipeline - - - -#export RLMachine, RLOffline, WeeklyEpisodes - - -#Base.@kwdef struct WeeklyEpisodes <: RLMachine -# _params::Dict = Dict() -# _model::Dict = Dict() -#end - -include("offlinerls.jl") -using .OfflineRLs -export DiscreteRLOffline, fit! -export driver, listdiscreateagents -export crossvalidateRL - -end # module diff --git a/examples/AutoOfflineRL/src/offlinerls.jl b/examples/AutoOfflineRL/src/offlinerls.jl deleted file mode 100644 index d0a96127..00000000 --- a/examples/AutoOfflineRL/src/offlinerls.jl +++ /dev/null @@ -1,279 +0,0 @@ -module OfflineRLs - -using AutoOfflineRL -using PythonCall -import Statistics -using Parquet -using Distributed -using DataFrames: DataFrame, dropmissing -using Random -using CSV -using Dates - -using ..AbsTypes -using ..Utils -import ..AbsTypes: fit, fit!, transform, transform! - -import PythonCall -const PYC = PythonCall - -using ..Utils: nested_dict_merge - -export DiscreteRLOffline, fit!, transform!, fit, transform -export listdiscreateagents, driver -export crossvalidateRL - -const rl_dict = Dict{String, PYC.Py}() -const metric_dict = Dict{String, PYC.Py}() - -const PYRL = PYC.pynew() -const PYPD = PYC.pynew() -const PYNP = PYC.pynew() -const PYDT = PYC.pynew() -const PYMT = PYC.pynew() -const PYSK = PYC.pynew() - - -function __init__() - PYC.pycopy!(PYRL, PYC.pyimport("d3rlpy.algos")) - PYC.pycopy!(PYDT, PYC.pyimport("d3rlpy.datasets")) - PYC.pycopy!(PYSK, PYC.pyimport("sklearn.model_selection")) - PYC.pycopy!(PYMT, PYC.pyimport("d3rlpy.metrics")) - PYC.pycopy!(PYPD, PYC.pyimport("pandas")) - PYC.pycopy!(PYNP, PYC.pyimport("numpy")) - - # OfflineRLs - metric_dict["cross_validate"] = PYSK.cross_validate - metric_dict["train_test_split"] = PYSK.train_test_split - metric_dict["td_error_scorer"] = PYMT.td_error_scorer - metric_dict["average_value_estimation_scorer"] = PYMT.average_value_estimation_scorer - metric_dict["get_cartpole"] = PYDT.get_cartpole - - rl_dict["DiscreteBC"] = PYRL - rl_dict["DQN"] = PYRL - rl_dict["NFQ"] = PYRL - rl_dict["DoubleDQN"] = PYRL - rl_dict["DiscreteBCQ"] = PYRL - rl_dict["DiscreteCQL"] = PYRL - rl_dict["DiscreteSAC"] = PYRL - #rl_dict["DiscreteRandomPolicy"] = PYRL -end - -mutable struct DiscreteRLOffline <: Learner - name::String - model::Dict{Symbol,Any} - - function DiscreteRLOffline(args=Dict{Symbol,Any}()) - default_args = Dict{Symbol,Any}( - :name => "DQN", - :tag => "RLOffline", - :rlagent => "DQN", - :iterations => 100, - :save_metrics => false, - :rlobjtrained => PYC.PyNULL, - :o_header => ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"], - :a_header => ["action"], - :r_header => ["reward"], - :save_model => false, - :runtime_args => Dict{Symbol, Any}( - :n_epochs => 5, - ), - :impl_args => Dict{Symbol,Any}( - :scaler => "min_max", - :use_gpu => false, - ) - ) - cargs = nested_dict_merge(default_args,args) - #datestring = Dates.format(now(), "yyyy-mm-dd-HH-MM") - cargs[:name] = cargs[:name]*"_"*randstring(3) - rlagent = cargs[:rlagent] - if !(rlagent in keys(rl_dict)) - println("error: $rlagent is not supported.") - println() - discreteagents() - error("Argument keyword error") - end - new(cargs[:name],cargs) - end -end - -function DiscreteRLOffline(rlagent::String, args::Dict) - DiscreteRLOffline(Dict(:rlagent => rlagent, :name => rlagent, args...)) -end - -function DiscreteRLOffline(rlagent::String; args...) - DiscreteRLOffline(Dict( - :rlagent => rlagent, - :name => rlagent, - args... - )) -end - -function listdiscreateagents() - println() - println("RL Discrete Agents:") - agents = keys(rl_dict) |> collect - [println(" ",agent," ") for agent in agents] - println("See d3rlpy python package for details about the agent arguments.") - nothing -end - -function discreteagents() - println() - println("syntax: DiscreteRLOffline(name::String, args::Dict)") - println("and 'args' are the agent's parameters") - println("See d3rlpy python package for details about the agent arguments.") - println("use: listdiscreateagents() to get the available RL agents") -end - -function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector) - _observations = df |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - _actions = action_reward[1] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - _rewards = action_reward[2] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - ## inject end of data by terminal column - nrow, _ = size(df) - _terminals = zeros(Int, nrow) - _terminals[end] = 1 - _terminals = _terminals .|> PYC.float |> x -> PYNP.array(x, dtype = "int32") - ## create dataset for RLOffline - mdp_dataset = PYDT.MDPDataset( - observations = _observations, - actions = _actions, - rewards = _rewards, - terminals = _terminals, - ) - ## save params - agent.model[:mdp_dataset] = mdp_dataset - agent.model[:np_observations] = _observations - agent.model[:np_actions] = _actions - agent.model[:np_rewards] = _rewards - return mdp_dataset -end - -function checkheaders(agent::DiscreteRLOffline, df) - o_header = agent.model[:o_header] - a_header = agent.model[:a_header] - r_header = agent.model[:r_header] - dfnames = names(df) - [@assert header in dfnames "\"$header\" is not in data header" - for header in vcat(o_header, a_header, r_header)] -end - -function fit!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector)::Nothing - # check if headers exist - #checkheaders(agent::DiscreteRLOffline, df) - # create mdp data - nrow, ncol = size(df) - mdp_dataset = createmdpdata!(agent, df,action_reward) - ## prepare algorithm - runtime_args = agent.model[:runtime_args] - logging = agent.model[:save_metrics] - impl_args = copy(agent.model[:impl_args]) - rlagent = agent.model[:rlagent] - py_rlagent = getproperty(rl_dict[rlagent],rlagent) - pyrlobj = py_rlagent(;impl_args...) - pyrlobj.fit(mdp_dataset; save_metrics = logging, runtime_args... ) - ## save rl to model dictionary - agent.model[:rlobjtrained] = pyrlobj - agent.model[:nrow] = nrow - agent.model[:ncol] = ncol - ## save model to file - if agent.model[:save_model] == true - path = pkgdir(AutoOfflineRL) - agentname = agent.model[:name] - tag = agent.model[:tag] - fnmodel = "$path/model/$(agentname)_$(tag)_model.pt" - fnpolicy = "$path/model/$(agentname)_$(tag)_policy.pt" - pyrlobj.save_model(fnmodel) - pyrlobj.save_policy(fnpolicy) - end - return nothing -end - - -function transform!(agent::DiscreteRLOffline,df::DataFrame=DataFrame())::Vector - pyrlobj = agent.model[:rlobjtrained] - #o_header = agent.model[:o_header] - observations = df |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - res = map(observations) do obs - action = pyrlobj.predict(obs) - value = pyrlobj.predict_value([obs],action) - action = PYC.pyconvert.(Float64,action) - value = PYC.pyconvert.(Float64,value) - obs = PYC.pyconvert.(Float64,obs) - (;obs,action,value) - end - return res -end - -function prp_fit_transform(pipe::Machine, instances::DataFrame,actreward::Vector) - machines = pipe.model[:machines] - machine_args = pipe.model[:machine_args] - - current_instances = instances - trlength = length(machines) - for t_index in 1:(trlength - 1) - machine = createmachine(machines[t_index], machine_args) - fit!(machine, current_instances, actreward) - current_instances = transform!(machine, current_instances) - end - return current_instances -end - - -function driver() - path = pkgdir(AutoOfflineRL) - dataset = "$path/data/smalldata.parquet" - df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing - df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] - reward = df[:,["reward"]] |> deepcopy |> DataFrame - action = df[:,["action"]] |> deepcopy |> DataFrame - action_reward = DataFrame[action, reward] - agentname="NFQ" - agent = DiscreteRLOffline(agentname) - #fit_transform!(agent,df_input,action_reward) -end - -function crossvalidateRL(pp::Machine, dfobs::DataFrame, actreward::Vector) - pipe = deepcopy(pp) - features = deepcopy(dfobs) - machines = pipe.model[:machines] - agent = machines[end] - - runtime_args = agent.model[:runtime_args] - logging = agent.model[:save_metrics] - impl_args = copy(agent.model[:impl_args]) - rlagent = agent.model[:rlagent] - py_rlagent = getproperty(rl_dict[rlagent],rlagent) - pyrlobj = py_rlagent(;impl_args...) - - df_input = prp_fit_transform(pipe,features,actreward) - mdp_dataset = createmdpdata!(agent,df_input,actreward) - - td_error_scorer = PYMT.td_error_scorer - runconfig = Dict(:scorers=>Dict("td_error"=>td_error_scorer)) - score=pyrlobj.fit(mdp_dataset; - eval_episodes=mdp_dataset, - runtime_args...,runconfig...) - vals = pyconvert(Array,score) - mvals = [v[2]["td_error"] for v in vals] |> Statistics.mean - return mvals - #return mdp_dataset - #py_train_test_split = metric_dict["train_test_split"] - - #py_get_cartpole = metric_dict["get_cartpole"] - #dataset,_ = py_get_cartpole() - #train_episodes, test_episodes = py_train_test_split([mdp_dataset,mdp_dataset,mdp_dataset]) - #py_train_episodes,py_test_episodes = py_train_test_split(mdp_dataset; test_size=0.25) - - #pycrossvalidate = getproperty(metric_dict["cross_validate"],"cross_validate") - #td_error_scorer = PYMT.td_error_scorer - #runconfig = Dict(:scoring=>Dict(:td_error=>td_error_scorer), - # :fit_params=>Dict(:n_epochs=>1,:n_samples=>1,:n_splits=>1)) - #scores = pycrossvalidate(pyrlobj,mdp_dataset; runconfig...) - ##pyrlobj.fit(mdp_dataset; save_metrics = logging, runtime_args... ) - #return scores -end - - -end diff --git a/examples/AutoOfflineRL/test/runtests.jl b/examples/AutoOfflineRL/test/runtests.jl deleted file mode 100644 index 97e2c086..00000000 --- a/examples/AutoOfflineRL/test/runtests.jl +++ /dev/null @@ -1,60 +0,0 @@ -module TestOfflineRL -using AutoOfflineRL -using Test -using DataFrames -using PythonCall -using Parquet -const PYC=PythonCall - -@testset "Load Agents with Default Params" begin - for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) - @info "loading $agentid default params" - rlagent = DiscreteRLOffline(agentid) - @test typeof(rlagent) <: AutoOfflineRL.Learner - end -end - - -@testset "Load Agents with Param Args" begin - println() - for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) - @info "loading $agentid with customized params" - rlagent = DiscreteRLOffline(agentid, - Dict(:name=>agentid, - :iterations=>10000, - :epochs=>100) - ) - @test typeof(rlagent) <: AutoOfflineRL.Learner - end -end - -@testset "Test Exceptions" begin - @test_throws ErrorException DiscreteRLOffline("dummy") -end - -@testset "Test Agent fit!/transform Runs" begin - println() - path = pkgdir(AutoOfflineRL) - dataset = "$path/data/smalldata.parquet" - df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing - df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] - reward = df[:,["reward"]] |> deepcopy |> DataFrame - action = df[:,["action"]] |> deepcopy |> DataFrame - action_reward = DataFrame[action, reward] - for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) - @info "training $agentid" - agent = DiscreteRLOffline(agentid; save_model=false,runtime_args=Dict(:n_epochs=>1,:verbose=>false, :show_progress=>true)) - o_header = agent.model[:o_header] - fit!(agent,df_input,action_reward) - @test agent.model[:rlobjtrained] !== PYC.PyNULL - @info "transform $agentid" - adf = df_input[1:2,:] - if agentid != "DiscreteBC" - res = AutoOfflineRL.transform!(agent,adf) - @test typeof(res[1]) .== NamedTuple{(:obs,:action, :value), Tuple{Vector{Float64},Vector{Float64}, Vector{Float64}}} - end - end -end - - -end From 15787cd7563ab49a89dcddfb9b8650790c64b10b Mon Sep 17 00:00:00 2001 From: ppalmes Date: Wed, 21 Jun 2023 02:38:26 +0100 Subject: [PATCH 11/12] crossvalidation data and algo Signed-off-by: ppalmes --- 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zHw&K(BY}yJm&wK2!@|Xio1P~whK?tHa2-$8;5uSe0Xu=4p6`_=ldGkzwSxsW7lE5m zl%AKt%EFVI@b@*Nt&_XUuLhIDB}aER>t9UB5 zwV5m|-7Yyft~Ob)3%@!{AguP7kDrfBEI!Q7&G^6L`A-l39!xF{CigILErCG%?)f|7 z?|%OdxvNC{co<_+%ISUw{Tk2ju*qY@5-$vk`tgsj|BUl@4F1|^IUNS2r!oEhuK&9M ze+RDKd{B@&$cau~{Py9Nz5d(vww($Qy<_v)+DQwH?b0Mx>y&{OQ e>sh!syIZ?S*x3t7O2|q`5)A1H1id4t_x}&#UH#So diff --git a/AutoOfflineRL/examples/Project.toml b/AutoOfflineRL/examples/Project.toml index 1e27f477..6c6d180b 100644 --- a/AutoOfflineRL/examples/Project.toml +++ b/AutoOfflineRL/examples/Project.toml @@ -1,6 +1,7 @@ [deps] AutoMLPipeline = "08437348-eef5-4817-bc1b-d4e9459680d6" AutoOfflineRL = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" diff --git a/AutoOfflineRL/examples/script.jl b/AutoOfflineRL/examples/script.jl index 0be867b7..787c1390 100644 --- a/AutoOfflineRL/examples/script.jl +++ b/AutoOfflineRL/examples/script.jl @@ -8,6 +8,7 @@ nprocs() == 1 && addprocs() using Parquet using DataFrames end + @everywhere begin # load preprocessing elements #### Scaler @@ -35,14 +36,26 @@ path = pkgdir(AutoOfflineRL) dataset = "$path/data/smalldata.parquet" df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing -df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] +#df = df[:,["day", "hour", "minute", "dow"]] +#df.sensor1 = rand(1:500,srow) +#df.sensor2 = rand(1:200,srow) +#df.sensor3 = rand(1:100,srow) +#df.action = rand([10,50,100],srow) +#df.reward = rand(srow) + +srow,_ = size(df) +observation = df[:, ["day", "hour", "minute", "dow", "sensor1", "sensor2", "sensor3"]] reward = df[:,["reward"]] |> deepcopy |> DataFrame action = df[:,["action"]] |> deepcopy |> DataFrame -action_reward = DataFrame[action, reward] +_terminals = zeros(Int,srow) +_terminals[collect(100:1000:9000)] .= 1 +_terminals[end] = 1 +dterminal = DataFrame(terminal=_terminals) +action_reward_terminal = DataFrame[action, reward, dterminal] agent = DiscreteRLOffline("NFQ") pipe = (numf |> mx |> pca) |> agent -crossvalidateRL(pipe,df_input,action_reward) +crossvalidateRL(pipe,observation,action_reward_terminal) function pipelinesearch() agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] @@ -54,11 +67,11 @@ function pipelinesearch() try rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) rlpipeline = ((numf |> sc |> xt)) |> rlagent - res = crossvalidateRL(rlpipeline,df_input,action_reward) + res = crossvalidateRL(rlpipeline,observation,action_reward_terminal) scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] pname = "$scn |> $xtn |> $lrn" if !isnan(res) - DataFrame(pipeline=pname,perf=res) + DataFrame(pipeline=pname,td_error=res) else DataFrame() end @@ -69,9 +82,9 @@ function pipelinesearch() end end end - sort!(dfresults,:perf,rev=false) + #sort!(dfresults,:percent_action_matches,rev=true) return dfresults end dftable= pipelinesearch() +sort!(dftable,:td_error,rev=false) show(dftable,allcols=true,allrows=true,truncate=0) - diff --git a/AutoOfflineRL/src/offlinerls.jl b/AutoOfflineRL/src/offlinerls.jl index d0a96127..83348f53 100644 --- a/AutoOfflineRL/src/offlinerls.jl +++ b/AutoOfflineRL/src/offlinerls.jl @@ -46,6 +46,7 @@ function __init__() metric_dict["cross_validate"] = PYSK.cross_validate metric_dict["train_test_split"] = PYSK.train_test_split metric_dict["td_error_scorer"] = PYMT.td_error_scorer + metric_dict["discrete_action_match_scorer"] = PYMT.discrete_action_match_scorer metric_dict["average_value_estimation_scorer"] = PYMT.average_value_estimation_scorer metric_dict["get_cartpole"] = PYDT.get_cartpole @@ -76,7 +77,7 @@ mutable struct DiscreteRLOffline <: Learner :r_header => ["reward"], :save_model => false, :runtime_args => Dict{Symbol, Any}( - :n_epochs => 5, + :n_epochs => 3, ), :impl_args => Dict{Symbol,Any}( :scaler => "min_max", @@ -126,15 +127,11 @@ function discreteagents() println("use: listdiscreateagents() to get the available RL agents") end -function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector) +function createmdpdata!(agent::DiscreteRLOffline, df::DataFrame, action_reward_term::Vector) _observations = df |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - _actions = action_reward[1] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - _rewards = action_reward[2] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") - ## inject end of data by terminal column - nrow, _ = size(df) - _terminals = zeros(Int, nrow) - _terminals[end] = 1 - _terminals = _terminals .|> PYC.float |> x -> PYNP.array(x, dtype = "int32") + _actions = action_reward_term[1] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _rewards = action_reward_term[2] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") + _terminals = action_reward_term[3] |> Array .|> PYC.float |> x -> PYNP.array(x, dtype = "float32") ## create dataset for RLOffline mdp_dataset = PYDT.MDPDataset( observations = _observations, @@ -159,12 +156,12 @@ function checkheaders(agent::DiscreteRLOffline, df) for header in vcat(o_header, a_header, r_header)] end -function fit!(agent::DiscreteRLOffline, df::DataFrame, action_reward::Vector)::Nothing +function fit!(agent::DiscreteRLOffline, df::DataFrame, action_reward_term::Vector)::Nothing # check if headers exist #checkheaders(agent::DiscreteRLOffline, df) # create mdp data nrow, ncol = size(df) - mdp_dataset = createmdpdata!(agent, df,action_reward) + mdp_dataset = createmdpdata!(agent, df,action_reward_term) ## prepare algorithm runtime_args = agent.model[:runtime_args] logging = agent.model[:save_metrics] @@ -206,7 +203,7 @@ function transform!(agent::DiscreteRLOffline,df::DataFrame=DataFrame())::Vector return res end -function prp_fit_transform(pipe::Machine, instances::DataFrame,actreward::Vector) +function prp_fit_transform(pipe::Machine, instances::DataFrame,actrewterm::Vector) machines = pipe.model[:machines] machine_args = pipe.model[:machine_args] @@ -214,7 +211,7 @@ function prp_fit_transform(pipe::Machine, instances::DataFrame,actreward::Vector trlength = length(machines) for t_index in 1:(trlength - 1) machine = createmachine(machines[t_index], machine_args) - fit!(machine, current_instances, actreward) + fit!(machine, current_instances, actrewterm) current_instances = transform!(machine, current_instances) end return current_instances @@ -234,44 +231,48 @@ function driver() #fit_transform!(agent,df_input,action_reward) end -function crossvalidateRL(pp::Machine, dfobs::DataFrame, actreward::Vector) - pipe = deepcopy(pp) - features = deepcopy(dfobs) - machines = pipe.model[:machines] - agent = machines[end] - +function traintesteval(agent::DiscreteRLOffline,mdp_dataset::Py) runtime_args = agent.model[:runtime_args] logging = agent.model[:save_metrics] impl_args = copy(agent.model[:impl_args]) rlagent = agent.model[:rlagent] py_rlagent = getproperty(rl_dict[rlagent],rlagent) pyrlobj = py_rlagent(;impl_args...) - - df_input = prp_fit_transform(pipe,features,actreward) - mdp_dataset = createmdpdata!(agent,df_input,actreward) + py_train_test_split = metric_dict["train_test_split"] + trainepisodes,testepisodes = py_train_test_split(mdp_dataset) td_error_scorer = PYMT.td_error_scorer + discrete_action_match_scorer = PYMT.discrete_action_match_scorer runconfig = Dict(:scorers=>Dict("td_error"=>td_error_scorer)) - score=pyrlobj.fit(mdp_dataset; - eval_episodes=mdp_dataset, + #runconfig = Dict(:scorers=>Dict("metric"=>discrete_action_match_scorer)) + score=pyrlobj.fit(trainepisodes; + eval_episodes=testepisodes, runtime_args...,runconfig...) vals = pyconvert(Array,score) mvals = [v[2]["td_error"] for v in vals] |> Statistics.mean + #mvals = [v[2]["metric"] for v in vals] |> Statistics.mean return mvals - #return mdp_dataset - #py_train_test_split = metric_dict["train_test_split"] - - #py_get_cartpole = metric_dict["get_cartpole"] - #dataset,_ = py_get_cartpole() - #train_episodes, test_episodes = py_train_test_split([mdp_dataset,mdp_dataset,mdp_dataset]) - #py_train_episodes,py_test_episodes = py_train_test_split(mdp_dataset; test_size=0.25) - - #pycrossvalidate = getproperty(metric_dict["cross_validate"],"cross_validate") - #td_error_scorer = PYMT.td_error_scorer - #runconfig = Dict(:scoring=>Dict(:td_error=>td_error_scorer), - # :fit_params=>Dict(:n_epochs=>1,:n_samples=>1,:n_splits=>1)) - #scores = pycrossvalidate(pyrlobj,mdp_dataset; runconfig...) - ##pyrlobj.fit(mdp_dataset; save_metrics = logging, runtime_args... ) +end + +function crossvalidateRL(pp::Machine, dfobs::DataFrame, actrewterm::Vector; cv=3) + pipe = deepcopy(pp) + features = deepcopy(dfobs) + machines = pipe.model[:machines] + agent = machines[end] + + df_input = prp_fit_transform(pipe,features,actrewterm) + mdp_dataset = createmdpdata!(agent,df_input,actrewterm) + + scores= [traintesteval(agent,mdp_dataset) for i in 1:cv] + return Statistics.mean(scores) + + #pyskcrossvalidate = metric_dict["cross_validate"] + #td_error_scorer = metric_dict["td_error_scorer"] + #average_value_estimation_scorer = metric_dict["average_value_estimation_scorer"] + #runconfig = Dict(:scoring=>Dict("td_error"=>td_error_scorer, + # "value_scale"=>average_value_estimation_scorer), + # :fit_params=>Dict("n_epochs"=>1)) + #scores = pyskcrossvalidate(pyrlobj,mdp_dataset; runconfig...) #return scores end diff --git a/AutoOfflineRL/test/runtests.jl b/AutoOfflineRL/test/runtests.jl index 97e2c086..97708f9d 100644 --- a/AutoOfflineRL/test/runtests.jl +++ b/AutoOfflineRL/test/runtests.jl @@ -37,15 +37,20 @@ end path = pkgdir(AutoOfflineRL) dataset = "$path/data/smalldata.parquet" df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing - df_input = df[:, ["day", "hour", "minute", "dow", "metric1", "metric2", "metric3", "metric4"]] + srow,_ = size(df) + df_input = df[:, ["day", "hour", "minute", "dow", "sensor1", "sensor2", "sensor3"]] reward = df[:,["reward"]] |> deepcopy |> DataFrame action = df[:,["action"]] |> deepcopy |> DataFrame - action_reward = DataFrame[action, reward] + _terminals = zeros(Int,srow) + _terminals[collect(100:1000:9000)] .= 1 + _terminals[end] = 1 + dterminal = DataFrame(terminal=_terminals) + action_reward_terminal = DataFrame[action, reward, dterminal] for agentid in keys(AutoOfflineRL.OfflineRLs.rl_dict) @info "training $agentid" agent = DiscreteRLOffline(agentid; save_model=false,runtime_args=Dict(:n_epochs=>1,:verbose=>false, :show_progress=>true)) o_header = agent.model[:o_header] - fit!(agent,df_input,action_reward) + fit!(agent,df_input,action_reward_terminal) @test agent.model[:rlobjtrained] !== PYC.PyNULL @info "transform $agentid" adf = df_input[1:2,:] From cab3a6623a031a22db8f0b1236b8032302b7170e Mon Sep 17 00:00:00 2001 From: ppalmes Date: Thu, 22 Jun 2023 11:25:37 +0100 Subject: [PATCH 12/12] adding notebook presentation --- AutoOfflineRL/examples/Project.toml | 1 + AutoOfflineRL/examples/demo.jl | 90 + AutoOfflineRL/examples/offlinerl.png | Bin 0 -> 581085 bytes AutoOfflineRL/examples/presentation.ipynb | 3164 ++++ .../examples/presentation.slides.html | 15548 ++++++++++++++++ AutoOfflineRL/examples/rl.png | Bin 0 -> 158512 bytes 6 files changed, 18803 insertions(+) create mode 100644 AutoOfflineRL/examples/demo.jl create mode 100644 AutoOfflineRL/examples/offlinerl.png create mode 100644 AutoOfflineRL/examples/presentation.ipynb create mode 100644 AutoOfflineRL/examples/presentation.slides.html create mode 100644 AutoOfflineRL/examples/rl.png diff --git a/AutoOfflineRL/examples/Project.toml b/AutoOfflineRL/examples/Project.toml index 6c6d180b..9180c1f6 100644 --- a/AutoOfflineRL/examples/Project.toml +++ b/AutoOfflineRL/examples/Project.toml @@ -4,5 +4,6 @@ AutoOfflineRL = "4680bba7-2b59-4a6e-a544-0ebac8b8cdd3" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" Parquet = "626c502c-15b0-58ad-a749-f091afb673ae" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" diff --git a/AutoOfflineRL/examples/demo.jl b/AutoOfflineRL/examples/demo.jl new file mode 100644 index 00000000..787c1390 --- /dev/null +++ b/AutoOfflineRL/examples/demo.jl @@ -0,0 +1,90 @@ +using Distributed + +nprocs() == 1 && addprocs() + +@everywhere begin + using AutoOfflineRL + using AutoMLPipeline + using Parquet + using DataFrames +end + +@everywhere begin + # load preprocessing elements + #### Scaler + rb = SKPreprocessor("RobustScaler"); + pt = SKPreprocessor("PowerTransformer"); + norm = SKPreprocessor("Normalizer"); + mx = SKPreprocessor("MinMaxScaler"); + std = SKPreprocessor("StandardScaler") + ##### Column selector + catf = CatFeatureSelector(); + numf = NumFeatureSelector(); + ## load filters + ##### Decomposition + #apca = SKPreprocessor("PCA",Dict(:autocomponent=>true,:name=>"autoPCA")); + #afa = SKPreprocessor("FactorAnalysis",Dict(:autocomponent=>true,:name=>"autoFA")); + #aica = SKPreprocessor("FastICA",Dict(:autocomponent=>true,:name=>"autoICA")); + pca = SKPreprocessor("PCA"); + fa = SKPreprocessor("FactorAnalysis"); + ica = SKPreprocessor("FastICA"); + noop = Identity(Dict(:name => "Noop")); +end + +# load dataset +path = pkgdir(AutoOfflineRL) +dataset = "$path/data/smalldata.parquet" +df = Parquet.read_parquet(dataset) |> DataFrame |> dropmissing + +#df = df[:,["day", "hour", "minute", "dow"]] +#df.sensor1 = rand(1:500,srow) +#df.sensor2 = rand(1:200,srow) +#df.sensor3 = rand(1:100,srow) +#df.action = rand([10,50,100],srow) +#df.reward = rand(srow) + +srow,_ = size(df) +observation = df[:, ["day", "hour", "minute", "dow", "sensor1", "sensor2", "sensor3"]] +reward = df[:,["reward"]] |> deepcopy |> DataFrame +action = df[:,["action"]] |> deepcopy |> DataFrame +_terminals = zeros(Int,srow) +_terminals[collect(100:1000:9000)] .= 1 +_terminals[end] = 1 +dterminal = DataFrame(terminal=_terminals) +action_reward_terminal = DataFrame[action, reward, dterminal] + +agent = DiscreteRLOffline("NFQ") +pipe = (numf |> mx |> pca) |> agent +crossvalidateRL(pipe,observation,action_reward_terminal) + +function pipelinesearch() + agentnames = ["DiscreteCQL","NFQ","DoubleDQN","DiscreteSAC","DiscreteBCQ","DiscreteBC","DQN"] + scalers = [rb,pt,norm,std,mx,noop] + extractors = [pca,ica,fa,noop] + dfresults = @sync @distributed (vcat) for agentname in agentnames + @distributed (vcat) for sc in scalers + @distributed (vcat) for xt in extractors + try + rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1))) + rlpipeline = ((numf |> sc |> xt)) |> rlagent + res = crossvalidateRL(rlpipeline,observation,action_reward_terminal) + scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4] + pname = "$scn |> $xtn |> $lrn" + if !isnan(res) + DataFrame(pipeline=pname,td_error=res) + else + DataFrame() + end + catch e + println("error in $agentname") + DataFrame() + end + end + end + end + #sort!(dfresults,:percent_action_matches,rev=true) + return dfresults +end +dftable= pipelinesearch() +sort!(dftable,:td_error,rev=false) +show(dftable,allcols=true,allrows=true,truncate=0) diff --git a/AutoOfflineRL/examples/offlinerl.png b/AutoOfflineRL/examples/offlinerl.png new file mode 100644 index 0000000000000000000000000000000000000000..526a9cad2aec9185489f09f6e32f6eda86499517 GIT binary patch literal 581085 zcmeFYW0+-2wl17DD{b4Em9{gh(l#q?+qP}nwr$(Cty{Hs_vyapp6|!$-``x%d}73i z@s2kzB4Wi{bA`xAiNZo*LID5(z>13r$pHXB=m7u#0YQNM)*}t8Jd_I004-EBq)Qc$d90As>>17PZ#)af}DUve-nVl?|@(t_=X>k0L(w2g^Hrl zrqUf)8A|TRM@_D0Bx}Bm22DBIs!~m5CC9hqao~36VRzK^mHsl7VrO*O0qp$>S*em9 zWC{>XM;Y561j-;%RLCGE4G4G)%*f{houo`|I4~#zr~croj9>-4o)MxpIS^k4V2zijCm`OgeXvDN1iD@d57Qoa 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{ + "cell_type": "markdown", + "id": "36560868", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Preliminaries\n" + ] + }, + { + "cell_type": "markdown", + "id": "5a4b43d3", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Online RL vs Offline RL" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7010ee90", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Online RL\n", + "\n", + "\"online\n", + "\n", + "- maximize return (accumulation of sum of discounted current and future rewards)\n", + "- each observation is part of a sequence of trajectory\n", + "- each action influences future observations and accumulated rewards\n", + "- unlike in typical ML problem where the objective is to make one time prediction of the action to take, RL makes a series of predictions dynamically as it receives observations and optimizes accumulation of corresponding rewards\n", + "\n", + "ref: https://rail.eecs.berkeley.edu/deeprlcourse/" + ] + }, + { + "cell_type": "markdown", + "id": "01fa7d38", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Offline RL\n", + "\n", + "\"offline\n", + "\n", + "- in online RL, new data is collected for policy update\n", + "- in off-policy RL, some old data are retained together with new data for policy update\n", + "- in offline RL, all data is collected in advanced to train an RL agent for optimal policy by sampling episodes\n", + "\n", + "ref: https://rail.eecs.berkeley.edu/deeprlcourse/" + ] + }, + { + "cell_type": "markdown", + "id": "be0e2f21", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Why Offline RL\n", + "\n", + "- Cost: it can be too expensive to interact repeatedly and explore certain environment such as workload or resource management in the cloud but cheaper to collect logs and statistics for offline RL learning\n", + "- Risk: it can be risky to train an agent in autonomous driving and robotic operations\n", + "- Technological advancement in batch learning with deep learning architectures that can scale-up effectively for large datasets" + ] + }, + { + "cell_type": "markdown", + "id": "97842e27", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Major Objective\n", + "\n", + "Given a dataset containing trajectories, create an AutoMLPipeline wrapper function for offline RL to make it trivial to search for the best data processing pipeline for offline RL application." + ] + }, + { + "cell_type": "markdown", + "id": "293d109d", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Load packages " + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "f827b577-0f46-40e7-ab51-ebba5ed919c4", + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "using Distributed\n", + "\n", + "nprocs() == 1 && addprocs() \n", + "\n", + "@everywhere begin\n", + " using AutoOfflineRL\n", + " using AutoMLPipeline\n", + " using Parquet\n", + " using DataFrames\n", + "end" + ] + }, + { + "cell_type": "markdown", + "id": "fa381a6e", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Load Preprocessing Elements" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "6d7d36e9-07c4-4f69-8c18-b90af19dc6e1", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "@everywhere begin\n", + " #### Scaler\n", + " rb = SKPreprocessor(\"RobustScaler\");\n", + " pt = SKPreprocessor(\"PowerTransformer\");\n", + " norm = SKPreprocessor(\"Normalizer\");\n", + " mx = SKPreprocessor(\"MinMaxScaler\");\n", + " std = SKPreprocessor(\"StandardScaler\")\n", + " ##### Column selector\n", + " catf = CatFeatureSelector();\n", + " numf = NumFeatureSelector();\n", + " #### feature extractors\n", + " pca = SKPreprocessor(\"PCA\");\n", + " fa = SKPreprocessor(\"FactorAnalysis\");\n", + " ica = SKPreprocessor(\"FastICA\");\n", + " noop = Identity(Dict(:name => \"Noop\"));\n", + " #### ML/RL agents\n", + " rf = RandomForest()\n", + " tree = PrunedTree()\n", + " dqn = DiscreteRLOffline(\"DQN\")\n", + " sac = DiscreteRLOffline(\"DiscreteSAC\")\n", + "end" + ] + }, + { + "cell_type": "markdown", + "id": "f27f579c", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Load Offline Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "65e72008-c3d6-44f8-80fa-32d9b2cc81ce", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "

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Consider increasing tolerance or the maximum number of iterations.\n", + " warnings.warn(\n", + "\r", + "Epoch 1/3: 0%| | 0/218 [00:00 std |> ica) |> dqn\n", + "tderror=crossvalidateRL(pipe,observation,action_reward_terminal)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "57dfacef", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.2257244226676562" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tderror" + ] + }, + { + "cell_type": "markdown", + "id": "b40ec59d", + "metadata": {}, + "source": [ + "The TD error is the difference between the agent's current estimate and target value" + ] + }, + { + "cell_type": "markdown", + "id": "6a9ec189", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Find optimal OfflineRL pipeline in parallel" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "633319f7", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 2:\t2023-06-21 09:17:39 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:39 [info ] Directory is created at d3rlpy_logs/DiscreteCQL_20230621091739\n", + " From worker 2:\t2023-06-21 09:17:39 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:39 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:39 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:39 [info ] Parameters are saved to d3rlpy_logs/DiscreteCQL_20230621091739/params.json params={'action_scaler': None, 'alpha': 1.0, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.77796113, 1.3371367 , 1.6107398 , 1.4524562 , 1.7445312 ,\n", + " From worker 2:\t 0.6457107 , 0.5352664 ]], dtype=float32), 'minimum': array([[-1.0338004 , -1.4315552 , -1.647037 , -1.5634333 , -1.7121451 ,\n", + " From worker 2:\t -0.6054753 , -0.31666145]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DiscreteCQL', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 8:\t2023-06-21 09:17:39 [debug ] RoundIterator is selected.\n", + " From worker 8:\t2023-06-21 09:17:39 [info ] Directory is created at d3rlpy_logs/DQN_20230621091739\n", + " From worker 8:\t2023-06-21 09:17:39 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:39 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:39 [info ] Directory is created at d3rlpy_logs/NFQ_20230621091739\n", + " From worker 3:\t2023-06-21 09:17:39 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 8:\t2023-06-21 09:17:39 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:39 [debug ] Building models...\n", + " From worker 8:\t2023-06-21 09:17:39 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:39 [debug ] Models have been built.\n", + " From worker 8:\t2023-06-21 09:17:39 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091739/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.7926923, 1.3371367, 1.6107398, 1.399293 , 1.7445312, 0.6543074,\n", + " From worker 8:\t 0.5231041]], dtype=float32), 'minimum': array([[-0.9144198, -1.320497 , -1.6457819, -1.4033899, -1.7121451,\n", + " From worker 8:\t -0.5812454, -0.2982593]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 3:\t2023-06-21 09:17:39 [info ] Parameters are saved to d3rlpy_logs/NFQ_20230621091739/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911 , 1.5049787 , 1.7117655 , 1.6942327 , 1.9783719 ,\n", + " From worker 3:\t 0.6704674 , 0.49065414]], dtype=float32), 'minimum': array([[-1.2627224, -1.320497 , -1.8827811, -1.7183032, -1.675721 ,\n", + " From worker 3:\t -0.6269234, -1.5822366]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'NFQ', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█████████████████| 218/218 [00:00<00:00, 373.42it/s, loss=3.83]\n", + " From worker 4:\t2023-06-21 09:17:39 [debug ] RoundIterator is selected.\n", + " From worker 4:\t2023-06-21 09:17:39 [info ] Directory is created at d3rlpy_logs/DoubleDQN_20230621091739\n", + " From worker 4:\t2023-06-21 09:17:39 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:17:39 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:17:39 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:17:39 [info ] Parameters are saved to d3rlpy_logs/DoubleDQN_20230621091739/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 1.7000638, 1.5026734, 1.7445312, 0.6457107,\n", + " From worker 4:\t 0.9709042]], dtype=float32), 'minimum': array([[-1.2627224 , -1.4646739 , -1.9264166 , -1.5791949 , -1.7102567 ,\n", + " From worker 4:\t -0.84071213, -1.0527257 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DoubleDQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 5:\t2023-06-21 09:17:39 [debug ] RoundIterator is selected.\n", + " From worker 5:\t2023-06-21 09:17:39 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091739\n", + " From worker 5:\t2023-06-21 09:17:39 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 5:\t2023-06-21 09:17:39 [debug ] Building models...\n", + " From worker 5:\t2023-06-21 09:17:39 [debug ] Models have been built.\n", + " From worker 5:\t2023-06-21 09:17:39 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091739/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[7.9269230e-01, 1.3371367e+00, 1.6107398e+00, 1.5551701e+00,\n", + " From worker 5:\t 1.7445312e+00, 4.1851148e-01, 3.9809948e-16]], dtype=float32), 'minimum': array([[-9.1441977e-01, -1.3695900e+00, -1.6457819e+00, -1.4034406e+00,\n", + " From worker 5:\t -1.6697341e+00, -6.5430743e-01, 5.7550873e-17]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 6:\t2023-06-21 09:17:39 [debug ] RoundIterator is selected.\n", + " From worker 6:\t2023-06-21 09:17:39 [info ] Directory is created at d3rlpy_logs/DiscreteBCQ_20230621091739\n", + " From worker 6:\t2023-06-21 09:17:39 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 7:\t2023-06-21 09:17:39 [debug ] RoundIterator is selected.\n", + " From worker 7:\t2023-06-21 09:17:39 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091739\n", + " From worker 7:\t2023-06-21 09:17:39 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 6:\t2023-06-21 09:17:39 [debug ] Building models...\n", + " From worker 7:\t2023-06-21 09:17:39 [debug ] Building models...\n", + " From worker 6:\t2023-06-21 09:17:39 [debug ] Models have been built.\n", + " From worker 7:\t2023-06-21 09:17:39 [debug ] Models have been built.\n", + " From worker 6:\t2023-06-21 09:17:39 [info ] Parameters are saved to d3rlpy_logs/DiscreteBCQ_20230621091739/params.json params={'action_flexibility': 0.3, 'action_scaler': None, 'batch_size': 32, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 1.6107398, 1.5380808, 1.7445312, 0.8475338,\n", + " From worker 6:\t 0.9709042]], dtype=float32), 'minimum': array([[-1.2627224, -1.4646739, -1.9264166, -1.5126448, -1.7102567,\n", + " From worker 6:\t -0.6457107, -1.0527257]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DiscreteBCQ', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 7:\t2023-06-21 09:17:39 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091739/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.4646739, 1.920096 , 1.5126448, 1.9783719, 0.6543074,\n", + " From worker 7:\t 1.1772403]], dtype=float32), 'minimum': array([[-1.2627224 , -1.5049787 , -1.6457819 , -1.5148648 , -1.7121451 ,\n", + " From worker 7:\t -0.81132424, -0.9709042 ]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 2:\t2023-06-21 09:17:39 [info ] DiscreteCQL_20230621091739: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 4.510704530488461e-05, 'time_algorithm_update': 0.00259503093334513, 'loss': 3.8033027670799044, 'time_step': 0.002666063264969292, 'td_error': 0.34984880769006144} step=218\n", + " From worker 2:\t2023-06-21 09:17:39 [info ] Model parameters are saved to d3rlpy_logs/DiscreteCQL_20230621091739/model_218.pt\n", + "Epoch 1/1: 100%|███████████████████| 69/69 [00:00<00:00, 402.88it/s, loss=2.29]\n", + " From worker 7:\terror in DiscreteBC\n", + "Epoch 1/1: 100%|███████████████| 190/190 [00:00<00:00, 446.94it/s, loss=0.0825]\n", + " From worker 2:\t2023-06-21 09:17:40 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:40 [info ] Directory is created at d3rlpy_logs/DiscreteCQL_20230621091740\n", + " From worker 2:\t2023-06-21 09:17:40 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:40 [debug ] Building models...\n", + "Epoch 1/1: 100%|████████████████| 218/218 [00:00<00:00, 435.33it/s, loss=0.197]\n", + " From worker 2:\t2023-06-21 09:17:40 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:40 [info ] Parameters are saved to d3rlpy_logs/DiscreteCQL_20230621091740/params.json params={'action_scaler': None, 'alpha': 1.0, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.7926923, 1.3371367, 1.6107398, 1.4524562, 1.7445312, 0.6543074,\n", + " From worker 2:\t 0.5231041]], dtype=float32), 'minimum': array([[-1.0338004 , -1.4315552 , -1.647037 , -1.5634333 , -1.7121451 ,\n", + " From worker 2:\t -0.5812454 , -0.31666145]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DiscreteCQL', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 8:\t2023-06-21 09:17:40 [info ] DQN_20230621091739: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 4.936017488178454e-05, 'time_algorithm_update': 0.0021454158582185443, 'loss': 0.08067038490584022, 'time_step': 0.002224294762862356, 'td_error': 0.349530275497165} step=190\n", + " From worker 8:\t2023-06-21 09:17:40 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091739/model_190.pt\n", + "Epoch 1/1: 100%|███████████████| 246/246 [00:00<00:00, 462.45it/s, loss=0.0699]\n", + " From worker 3:\t2023-06-21 09:17:40 [info ] NFQ_20230621091739: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 5.499157336873746e-05, 'time_algorithm_update': 0.0022020449332141, 'loss': 0.19771507529353877, 'time_step': 0.0022861465401605727, 'td_error': 0.4070726314259975} step=218\n", + " From worker 3:\t2023-06-21 09:17:40 [info ] Model parameters are saved to d3rlpy_logs/NFQ_20230621091739/model_218.pt\n", + " From worker 4:\t2023-06-21 09:17:40 [info ] DoubleDQN_20230621091739: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 5.0197771894253363e-05, 'time_algorithm_update': 0.002068161964416504, 'loss': 0.06939889395200624, 'time_step': 0.0021510085439294334, 'td_error': 0.3295037820149992} step=246\n", + " From worker 4:\t2023-06-21 09:17:40 [info ] Model parameters are saved to d3rlpy_logs/DoubleDQN_20230621091739/model_246.pt\n", + "Epoch 1/1: 100%|█| 95/95 [00:00<00:00, 141.16it/s, temp_loss=-4.58, temp=1.01, \n", + " From worker 8:\t2023-06-21 09:17:40 [debug ] RoundIterator is selected.\n", + " From worker 8:\t2023-06-21 09:17:40 [info ] Directory is created at d3rlpy_logs/DQN_20230621091740\n", + " From worker 8:\t2023-06-21 09:17:40 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 8:\t2023-06-21 09:17:40 [debug ] Building models...\n", + " From worker 8:\t2023-06-21 09:17:40 [debug ] Models have been built.\n", + " From worker 8:\t2023-06-21 09:17:40 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091740/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 2.2819278, 1.5126448, 1.7445312, 0.6543074,\n", + " From worker 8:\t 1.0527257]], dtype=float32), 'minimum': array([[-1.2627224 , -1.4646739 , -1.6457819 , -1.5634333 , -1.7121451 ,\n", + " From worker 8:\t -0.94803315, -0.9709042 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 5:\t2023-06-21 09:17:40 [info ] DiscreteSAC_20230621091739: epoch=1 step=95 epoch=1 metrics={'time_sample_batch': 8.008103621633429e-05, 'time_algorithm_update': 0.006943519491898386, 'temp_loss': -4.5871779692800425, 'temp': 1.0145784817243877, 'critic_loss': 16517171348490.78, 'actor_loss': -15285661198961.18, 'time_step': 0.007063471643548262, 'td_error': 9.846342275558801e+26} step=95\n", + " From worker 5:\t2023-06-21 09:17:40 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091739/model_95.pt\n", + " From worker 3:\t2023-06-21 09:17:40 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:40 [info ] Directory is created at d3rlpy_logs/NFQ_20230621091740\n", + " From worker 3:\t2023-06-21 09:17:40 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:40 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:40 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:40 [info ] Parameters are saved to d3rlpy_logs/NFQ_20230621091740/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[7.9787636e-01, 1.3371367e+00, 1.6107398e+00, 1.5502471e+00,\n", + " From worker 3:\t 1.7445312e+00, 6.5430743e-01, 3.9809948e-16]], dtype=float32), 'minimum': array([[-9.1441977e-01, -1.3088119e+00, -1.5569713e+00, -1.6544964e+00,\n", + " From worker 3:\t -1.6697341e+00, -5.2033156e-01, -1.6346980e-15]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'NFQ', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█████████████████| 246/246 [00:00<00:00, 288.89it/s, loss=3.51]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 4:\t2023-06-21 09:17:40 [debug ] RoundIterator is selected.\n", + " From worker 4:\t2023-06-21 09:17:40 [info ] Directory is created at d3rlpy_logs/DoubleDQN_20230621091740\n", + " From worker 4:\t2023-06-21 09:17:40 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:17:40 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:17:40 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:17:40 [info ] Parameters are saved to d3rlpy_logs/DoubleDQN_20230621091740/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.6571574, 1.3371367, 1.7000638, 1.5026734, 1.7445312, 0.6543074,\n", + " From worker 4:\t 0.5446731]], dtype=float32), 'minimum': array([[-1.0338004 , -1.4315552 , -1.647037 , -1.4524562 , -1.7102567 ,\n", + " From worker 4:\t -0.57227874, -0.27350825]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DoubleDQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|██████████████████| 190/190 [00:00<00:00, 358.94it/s, loss=3.9]\n", + " From worker 2:\t2023-06-21 09:17:40 [info ] DiscreteCQL_20230621091740: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 4.539113295705695e-05, 'time_algorithm_update': 0.0027033605073627674, 'loss': 3.856140062683507, 'time_step': 0.0027752098284269636, 'td_error': 0.34433310867649836} step=190\n", + " From worker 2:\t2023-06-21 09:17:40 [info ] Model parameters are saved to d3rlpy_logs/DiscreteCQL_20230621091740/model_190.pt\n", + " From worker 2:\t2023-06-21 09:17:40 [debug ] RoundIterator is selected.\n", + " From worker 6:\t2023-06-21 09:17:40 [info ] DiscreteBCQ_20230621091739: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 5.255385143000905e-05, 'time_algorithm_update': 0.003364869249545462, 'loss': 3.489266607819534, 'time_step': 0.003448172313411061, 'td_error': 0.33191796381841476} step=246\n", + " From worker 6:\t2023-06-21 09:17:40 [info ] Model parameters are saved to d3rlpy_logs/DiscreteBCQ_20230621091739/model_246.pt\n", + " From worker 5:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 5:\t2023-06-21 09:17:41 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091741\n", + " From worker 5:\t2023-06-21 09:17:41 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 5:\t2023-06-21 09:17:41 [debug ] Building models...\n", + " From worker 5:\t2023-06-21 09:17:41 [debug ] Models have been built.\n", + " From worker 5:\t2023-06-21 09:17:41 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091741/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[7.9269230e-01, 1.3371367e+00, 1.6107398e+00, 1.6721407e+00,\n", + " From worker 5:\t 1.7445312e+00, 5.2033156e-01, 3.9809948e-16]], dtype=float32), 'minimum': array([[-1.0338004e+00, -1.3695900e+00, -1.6457819e+00, -1.4369258e+00,\n", + " From worker 5:\t -1.6697341e+00, -6.5430743e-01, -1.6346980e-15]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|████████████████| 218/218 [00:00<00:00, 534.99it/s, loss=0.086]\n", + " From worker 8:\t2023-06-21 09:17:41 [info ] DQN_20230621091740: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 4.157998146267112e-05, 'time_algorithm_update': 0.0017916532831454496, 'loss': 0.08464137135808347, 'time_step': 0.0018595686746299815, 'td_error': 0.3356154156937909} step=218\n", + "Epoch 1/1: 100%|██████████████| 190/190 [00:00<00:00, 511.98it/s, loss=6.3e+12]\n", + " From worker 8:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091740/model_218.pt\n", + " From worker 8:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 8:\t2023-06-21 09:17:41 [info ] Directory is created at d3rlpy_logs/DQN_20230621091741\n", + " From worker 8:\t2023-06-21 09:17:41 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 8:\t2023-06-21 09:17:41 [debug ] Building models...\n", + " From worker 8:\t2023-06-21 09:17:41 [debug ] Models have been built.\n", + " From worker 8:\t2023-06-21 09:17:41 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091741/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 2.2819278, 1.5126448, 1.7445312, 0.6543074,\n", + " From worker 8:\t 1.0527257]], dtype=float32), 'minimum': array([[-1.2627224 , -1.4646739 , -1.6457819 , -1.5634333 , -1.7121451 ,\n", + " From worker 8:\t -0.94803315, -0.9709042 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] NFQ_20230621091740: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 4.245858443410773e-05, 'time_algorithm_update': 0.0018767607839483963, 'loss': 6301005974905.263, 'time_step': 0.0019441830484490646, 'td_error': 1.069057827735549e+26} step=190\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/NFQ_20230621091740/model_190.pt\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] Directory is created at d3rlpy_logs/NFQ_20230621091741\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] Parameters are saved to d3rlpy_logs/NFQ_20230621091741/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 1.7117655, 1.5502471, 1.9783719, 0.6704674,\n", + " From worker 3:\t 0.4620808]], dtype=float32), 'minimum': array([[-1.2627224, -1.320497 , -1.8827811, -1.7183032, -1.675721 ,\n", + " From worker 3:\t -0.6269234, -1.5822366]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'NFQ', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████| 190/190 [00:00<00:00, 558.95it/s, loss=0.0826]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 4:\t2023-06-21 09:17:41 [info ] DoubleDQN_20230621091740: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 4.182489294754831e-05, 'time_algorithm_update': 0.001713250812731291, 'loss': 0.08083449318808944, 'time_step': 0.0017797846543161492, 'td_error': 0.3419192071373942} step=190\n", + " From worker 4:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/DoubleDQN_20230621091740/model_190.pt\n", + " From worker 4:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 4:\t2023-06-21 09:17:41 [info ] Directory is created at d3rlpy_logs/DoubleDQN_20230621091741\n", + " From worker 4:\t2023-06-21 09:17:41 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 6:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 6:\t2023-06-21 09:17:41 [info ] Directory is created at d3rlpy_logs/DiscreteBCQ_20230621091741\n", + " From worker 6:\t2023-06-21 09:17:41 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:17:41 [debug ] Building models...\n", + " From worker 6:\t2023-06-21 09:17:41 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:17:41 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:17:41 [info ] Parameters are saved to d3rlpy_logs/DoubleDQN_20230621091741/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 1.7000638, 1.5026734, 1.7445312, 0.6457107,\n", + " From worker 4:\t 0.9709042]], dtype=float32), 'minimum': array([[-1.2627224 , -1.4646739 , -1.9264166 , -1.5791949 , -1.7102567 ,\n", + " From worker 4:\t -0.84071213, -1.0527257 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DoubleDQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 6:\t2023-06-21 09:17:41 [debug ] Models have been built.\n", + " From worker 6:\t2023-06-21 09:17:41 [info ] Parameters are saved to d3rlpy_logs/DiscreteBCQ_20230621091741/params.json params={'action_flexibility': 0.3, 'action_scaler': None, 'batch_size': 32, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.7926923 , 1.3023561 , 1.6044478 , 1.4033899 , 1.7445312 ,\n", + " From worker 6:\t 0.57624644, 0.5446731 ]], dtype=float32), 'minimum': array([[-1.0338004 , -1.4315552 , -1.647037 , -1.4524562 , -1.7102567 ,\n", + " From worker 6:\t -0.6543074 , -0.27350825]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DiscreteBCQ', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 536.47it/s, loss=0.0956]\n", + " From worker 8:\t2023-06-21 09:17:41 [info ] DQN_20230621091741: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 4.1442179898603244e-05, 'time_algorithm_update': 0.0017878757704288587, 'loss': 0.09385682608282894, 'time_step': 0.0018546964050432959, 'td_error': 0.35432347418705135} step=218\n", + " From worker 8:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091741/model_218.pt\n", + "Epoch 1/1: 100%|████████████████| 218/218 [00:00<00:00, 516.61it/s, loss=0.223]\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] NFQ_20230621091741: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 4.212025108687375e-05, 'time_algorithm_update': 0.0018612518223053817, 'loss': 0.22377619776157065, 'time_step': 0.0019271406558675503, 'td_error': 0.49007505557494097} step=218\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/NFQ_20230621091741/model_218.pt\n", + "Epoch 1/1: 100%|█| 95/95 [00:00<00:00, 160.76it/s, temp_loss=-4.58, temp=1.01, \n", + "Epoch 1/1: 100%|███████████████| 246/246 [00:00<00:00, 546.30it/s, loss=0.0707]\n", + " From worker 5:\t2023-06-21 09:17:41 [info ] DiscreteSAC_20230621091741: epoch=1 step=95 epoch=1 metrics={'time_sample_batch': 6.808732685289885e-05, 'time_algorithm_update': 0.006103771611263877, 'temp_loss': -4.5871779692800425, 'temp': 1.0145784817243877, 'critic_loss': 3907441664086.2314, 'actor_loss': -4449662659185.179, 'time_step': 0.00620519989415219, 'td_error': 2.3705497272879246e+25} step=95\n", + " From worker 4:\t2023-06-21 09:17:41 [info ] DoubleDQN_20230621091741: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 4.2355157495514165e-05, 'time_algorithm_update': 0.001753175161718353, 'loss': 0.07013292414537532, 'time_step': 0.001821445255744748, 'td_error': 0.3307183182128285} step=246\n", + " From worker 4:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/DoubleDQN_20230621091741/model_246.pt\n", + " From worker 5:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091741/model_95.pt\n", + " From worker 5:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091741\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:41 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091741/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9381961 , 1.1401778 , 0.7587837 , 0.4004507 , 0.4045163 ,\n", + " From worker 3:\t 0.22754474, 0.77696633]], dtype=float32), 'minimum': array([[-0.46346295, -0.37498793, -0.32135844, -0.3676697 , -0.50954545,\n", + " From worker 3:\t -0.48041272, -0.51314104]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 4:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|███████████████████| 69/69 [00:00<00:00, 609.74it/s, loss=2.29]\n", + "Epoch 1/1: 100%|█████████████████| 190/190 [00:00<00:00, 304.40it/s, loss=3.91]\n", + " From worker 3:\terror in DiscreteBC\n", + " From worker 6:\t2023-06-21 09:17:41 [info ] DiscreteBCQ_20230621091741: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 4.2665632147538036e-05, 'time_algorithm_update': 0.0032078454368992854, 'loss': 3.859865134640744, 'time_step': 0.003275139708268015, 'td_error': 0.3572662849078008} step=190\n", + " From worker 6:\t2023-06-21 09:17:41 [info ] Model parameters are saved to d3rlpy_logs/DiscreteBCQ_20230621091741/model_190.pt\n", + " From worker 6:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:41 [info ] Directory is created at d3rlpy_logs/DiscreteCQL_20230621091741\n", + " From worker 2:\t2023-06-21 09:17:41 [debug ] Fitting scaler... scaler=min_max\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 2:\t2023-06-21 09:17:41 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:41 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:41 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:41 [info ] Parameters are saved to d3rlpy_logs/DiscreteCQL_20230621091741/params.json params={'action_scaler': None, 'alpha': 1.0, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 1.6107398, 1.5126448, 1.7445312, 0.6543074,\n", + " From worker 2:\t 0.9709042]], dtype=float32), 'minimum': array([[-1.2627224, -1.4646739, -1.9264166, -1.5380808, -1.7121451,\n", + " From worker 2:\t -0.8488413, -1.0527257]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DiscreteCQL', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█████████████████| 218/218 [00:00<00:00, 581.26it/s, loss=3.76]\n", + " From worker 2:\t2023-06-21 09:17:42 [info ] DiscreteCQL_20230621091741: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.6314630420929793e-05, 'time_algorithm_update': 0.0016727053791011145, 'loss': 3.727580152520346, 'time_step': 0.0017157727425251533, 'td_error': 0.3510580793225154} step=218\n", + " From worker 2:\t2023-06-21 09:17:42 [info ] Model parameters are saved to d3rlpy_logs/DiscreteCQL_20230621091741/model_218.pt\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:542: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n", + " From worker 2:\t warnings.warn(\n", + " From worker 5:\t2023-06-21 09:17:42 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091742\n", + " From worker 5:\t2023-06-21 09:17:42 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 5:\t2023-06-21 09:17:42 [debug ] Building models...\n", + " From worker 5:\t2023-06-21 09:17:42 [debug ] Models have been built.\n", + " From worker 5:\t2023-06-21 09:17:42 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091742/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 1.6107398, 1.5791949, 1.7445312, 0.8238525,\n", + " From worker 5:\t 0.9709042]], dtype=float32), 'minimum': array([[-1.2627224, -1.4646739, -1.9264166, -1.4369258, -1.675721 ,\n", + " From worker 5:\t -0.6543074, -1.0527257]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 4:\t2023-06-21 09:17:42 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091742\n", + " From worker 4:\t2023-06-21 09:17:42 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:17:42 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:17:42 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:17:42 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091742/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1. , 0.9285714, 1. , 1. , 1.0121458, 1. ,\n", + " From worker 4:\t 1. ]], dtype=float32), 'minimum': array([[ 0. , -0.71428573, -0.96666664, 0. , -1.0080972 ,\n", + " From worker 4:\t -0.99 , -0.98 ]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:123: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n", + " From worker 2:\t warnings.warn(\n", + " From worker 2:\t2023-06-21 09:17:42 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:42 [info ] Directory is created at d3rlpy_logs/DQN_20230621091742\n", + " From worker 2:\t2023-06-21 09:17:42 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:42 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:42 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:42 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091742/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.1024223e+12, 7.7798225e+11, 3.9527328e+12, 2.1446488e+12,\n", + " From worker 2:\t 6.3687953e+12, 2.6677488e+12, 4.8132826e+12]], dtype=float32), 'minimum': array([[-3.1024223e+12, -7.7798225e+11, -3.9527328e+12, -2.1446488e+12,\n", + " From worker 2:\t -7.4621214e+12, -2.8364922e+12, -3.2294689e+12]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 6:\t2023-06-21 09:17:42 [info ] Directory is created at d3rlpy_logs/DiscreteBCQ_20230621091742\n", + " From worker 6:\t2023-06-21 09:17:42 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 6:\t2023-06-21 09:17:42 [debug ] Building models...\n", + " From worker 6:\t2023-06-21 09:17:42 [debug ] Models have been built.\n", + " From worker 6:\t2023-06-21 09:17:42 [info ] Parameters are saved to d3rlpy_logs/DiscreteBCQ_20230621091742/params.json params={'action_flexibility': 0.3, 'action_scaler': None, 'batch_size': 32, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.7241911, 1.5049787, 1.6107398, 1.5380808, 1.7445312, 0.8475338,\n", + " From worker 6:\t 0.9709042]], dtype=float32), 'minimum': array([[-1.2627224, -1.4646739, -1.9264166, -1.5126448, -1.7102567,\n", + " From worker 6:\t -0.6457107, -1.0527257]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DiscreteBCQ', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████████| 69/69 [00:00<00:00, 784.02it/s, loss=2.33]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 4:\terror in DiscreteBC\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] RoundIterator is selected.\n", + " From worker 4:\t2023-06-21 09:17:43 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091743\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:17:43 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091743/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.7828405, 2.7306168, 2.8765562, 2.9279127, 4.3325047, 1.8230203,\n", + " From worker 4:\t 2.4710565]], dtype=float32), 'minimum': array([[-1.703448 , -2.7389183, -4.2741866, -4.1235604, -2.9592102,\n", + " From worker 4:\t -2.8719223, -2.5508852]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████████| 69/69 [00:00<00:00, 632.32it/s, loss=2.32]\n", + " From worker 4:\terror in DiscreteBC\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] RoundIterator is selected.\n", + " From worker 4:\t2023-06-21 09:17:43 [info ] Directory is created at d3rlpy_logs/DQN_20230621091743\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] Models have been built.\n", + "Epoch 1/1: 100%|████████████████| 246/246 [00:00<00:00, 619.05it/s, loss=0.069]\n", + " From worker 4:\t2023-06-21 09:17:43 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091743/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0. , 0.85714287, 1. , 0. , 1.0121458 ,\n", + " From worker 4:\t 1. , 1. ]], dtype=float32), 'minimum': array([[ 0. , -0.71428573, -0.96666664, 0. , -1.0080972 ,\n", + " From worker 4:\t -0.99 , -0.98 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 2:\t2023-06-21 09:17:43 [info ] DQN_20230621091742: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 4.376337780215876e-05, 'time_algorithm_update': 0.0015390084041812557, 'loss': 0.06846761285531812, 'time_step': 0.001607822208869748, 'td_error': 0.3295860414898109} step=246\n", + " From worker 2:\t2023-06-21 09:17:43 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091742/model_246.pt\n", + " From worker 2:\t2023-06-21 09:17:43 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|█| 109/109 [00:00<00:00, 175.98it/s, temp_loss=-.55, temp=0.993\n", + " From worker 5:\t2023-06-21 09:17:43 [info ] DiscreteSAC_20230621091742: epoch=1 step=109 epoch=1 metrics={'time_sample_batch': 6.348277450701512e-05, 'time_algorithm_update': 0.005577789534122572, 'temp_loss': -0.5449240399917604, 'temp': 0.9939265469892309, 'critic_loss': 4.354956706729504, 'actor_loss': -4.907961683535794, 'time_step': 0.005669665992806811, 'td_error': 0.3095081326876401} step=109\n", + " From worker 5:\t2023-06-21 09:17:43 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091742/model_109.pt\n", + " From worker 5:\t2023-06-21 09:17:43 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|██████████████████| 190/190 [00:00<00:00, 720.48it/s, loss=nan]\n", + "Epoch 1/1: 100%|█████████████████| 246/246 [00:00<00:00, 365.47it/s, loss=3.53]\n", + " From worker 4:\t2023-06-21 09:17:43 [info ] DQN_20230621091743: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 3.513787922106291e-05, 'time_algorithm_update': 0.0013250714854190224, 'loss': nan, 'time_step': 0.0013815679048237047, 'td_error': nan} step=190\n", + " From worker 4:\t2023-06-21 09:17:43 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091743/model_190.pt\n", + " From worker 4:\t2023-06-21 09:17:43 [debug ] RoundIterator is selected.\n", + " From worker 6:\t2023-06-21 09:17:43 [info ] DiscreteBCQ_20230621091742: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 3.9584268399370396e-05, 'time_algorithm_update': 0.0026645340570589393, 'loss': 3.5020599316775316, 'time_step': 0.002727894279045787, 'td_error': 0.33054662400169754} step=246\n", + " From worker 6:\t2023-06-21 09:17:43 [info ] Model parameters are saved to d3rlpy_logs/DiscreteBCQ_20230621091742/model_246.pt\n", + " From worker 6:\t2023-06-21 09:17:43 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:44 [info ] Directory is created at d3rlpy_logs/DQN_20230621091744\n", + " From worker 2:\t2023-06-21 09:17:44 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:44 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:44 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:44 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091744/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.1024223e+12, 7.7798225e+11, 3.9527328e+12, 2.1446488e+12,\n", + " From worker 2:\t 6.3067469e+12, 2.6677488e+12, 4.8132826e+12]], dtype=float32), 'minimum': array([[-3.1024223e+12, -7.7798225e+11, -3.9527328e+12, -2.1446488e+12,\n", + " From worker 2:\t -7.4621214e+12, -2.8364922e+12, -3.2294689e+12]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 5:\t2023-06-21 09:17:44 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091744\n", + " From worker 5:\t2023-06-21 09:17:44 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 5:\t2023-06-21 09:17:44 [debug ] Building models...\n", + " From worker 5:\t2023-06-21 09:17:44 [debug ] Models have been built.\n", + " From worker 5:\t2023-06-21 09:17:44 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091744/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.3023418 , 0.7144378 , 0.85504866, 0.8611191 , 0.7454196 ,\n", + " From worker 5:\t 0.5472575 , 0.28775597]], dtype=float32), 'minimum': array([[-0.60449946, -0.7369663 , -1.4143645 , -1.3660418 , -0.81751764,\n", + " From worker 5:\t -0.5384156 , -0.20355542]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|███████████████████| 78/78 [00:00<00:00, 838.04it/s, loss=2.26]\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 799.78it/s, loss=0.0928]\n", + " From worker 2:\t2023-06-21 09:17:44 [info ] DQN_20230621091744: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 3.022557004876093e-05, 'time_algorithm_update': 0.001195727138344301, 'loss': 0.09131083575102987, 'time_step': 0.0012449113600844638, 'td_error': 0.33717236146601487} step=218\n", + " From worker 2:\t2023-06-21 09:17:44 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091744/model_218.pt\n", + " From worker 2:\t2023-06-21 09:17:44 [debug ] RoundIterator is selected.\n", + " From worker 5:\terror in DiscreteBC\n", + " From worker 5:\t2023-06-21 09:17:44 [debug ] RoundIterator is selected.\n", + " From worker 4:\t2023-06-21 09:17:45 [info ] Directory is created at d3rlpy_logs/DQN_20230621091745\n", + " From worker 4:\t2023-06-21 09:17:45 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:17:45 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:17:45 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:17:45 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091745/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1. , 0.9285714, 1. , 1. , 1.0121458, 1. ,\n", + " From worker 4:\t 1. ]], dtype=float32), 'minimum': array([[ 0. , -0.71428573, -0.96666664, 0. , -1.0080972 ,\n", + " From worker 4:\t -0.99 , -0.98 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 6:\t2023-06-21 09:17:45 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091745\n", + " From worker 6:\t2023-06-21 09:17:45 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 6:\t2023-06-21 09:17:45 [debug ] Building models...\n", + " From worker 6:\t2023-06-21 09:17:45 [debug ] Models have been built.\n", + " From worker 6:\t2023-06-21 09:17:45 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091745/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[250.33273 , 260.5288 , 50.23776 , 29.988697, 10.523216,\n", + " From worker 6:\t 99.29697 , 73.98687 ]], dtype=float32), 'minimum': array([[-249.43936 , -274.04565 , -49.83971 , -30.540922, -12.57127 ,\n", + " From worker 6:\t -100.146736, -233.99266 ]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████████| 69/69 [00:00<00:00, 838.29it/s, loss=2.22]\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 784.27it/s, loss=0.0959]\n", + " From worker 6:\terror in DiscreteBC\n", + " From worker 4:\t2023-06-21 09:17:45 [info ] DQN_20230621091745: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.9512501637870018e-05, 'time_algorithm_update': 0.001221386664504305, 'loss': 0.09440876843424839, 'time_step': 0.0012693536390952014, 'td_error': 0.34156056917367106} step=218\n", + " From worker 4:\t2023-06-21 09:17:45 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091745/model_218.pt\n", + " From worker 4:\t2023-06-21 09:17:45 [debug ] RoundIterator is selected.\n", + " From worker 6:\t2023-06-21 09:17:45 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:46 [info ] Directory is created at d3rlpy_logs/DQN_20230621091746\n", + " From worker 2:\t2023-06-21 09:17:46 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:46 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:46 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:46 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091746/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.1024223e+12, 7.7798225e+11, 3.9527328e+12, 2.1446488e+12,\n", + " From worker 2:\t 5.5217808e+12, 2.6677488e+12, 4.8132826e+12]], dtype=float32), 'minimum': array([[-3.1024223e+12, -7.7798225e+11, -3.9527328e+12, -2.1446488e+12,\n", + " From worker 2:\t -7.4621214e+12, -2.8364922e+12, -3.2294689e+12]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 3:\t2023-06-21 09:17:46 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091746\n", + " From worker 3:\t2023-06-21 09:17:46 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:46 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:46 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:46 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091746/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32), 'minimum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████| 246/246 [00:00<00:00, 838.96it/s, loss=0.0849]\n", + " From worker 2:\t2023-06-21 09:17:46 [info ] DQN_20230621091746: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 2.669609659086398e-05, 'time_algorithm_update': 0.0011430755863344765, 'loss': 0.0840105459139478, 'time_step': 0.0011873080478451115, 'td_error': 0.33263165771910613} step=246\n", + " From worker 2:\t2023-06-21 09:17:46 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091746/model_246.pt\n", + "Epoch 1/1: 100%|████████████████████| 78/78 [00:00<00:00, 936.22it/s, loss=nan]\n", + " From worker 3:\terror in DiscreteBC\n", + " From worker 2:\t2023-06-21 09:17:47 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:47 [info ] Directory is created at d3rlpy_logs/DQN_20230621091747\n", + " From worker 2:\t2023-06-21 09:17:47 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:47 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:47 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:47 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091747/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.2017066 , 3.8068576 , 3.1260934 , 4.35434 , 2.6527042 ,\n", + " From worker 2:\t 1.3774092 , 0.32658172]], dtype=float32), 'minimum': array([[-2.1025314 , -2.8379962 , -2.7936583 , -2.3866355 , -3.2439196 ,\n", + " From worker 2:\t -2.464134 , -0.32029364]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 3:\t2023-06-21 09:17:47 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|███████████████| 246/246 [00:00<00:00, 895.66it/s, loss=0.0896]\n", + " From worker 2:\t2023-06-21 09:17:47 [info ] DQN_20230621091747: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 2.6294855567497934e-05, 'time_algorithm_update': 0.0010709723805993553, 'loss': 0.0887927300319439, 'time_step': 0.0011126607414183578, 'td_error': 0.3539537419557423} step=246\n", + " From worker 2:\t2023-06-21 09:17:47 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091747/model_246.pt\n", + " From worker 2:\t2023-06-21 09:17:47 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:48 [info ] Directory is created at d3rlpy_logs/DQN_20230621091748\n", + " From worker 3:\t2023-06-21 09:17:48 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:48 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:48 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:48 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091748/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32), 'minimum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|██████████████████| 218/218 [00:00<00:00, 848.79it/s, loss=nan]\n", + " From worker 3:\t2023-06-21 09:17:48 [info ] DQN_20230621091748: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.6303693788860918e-05, 'time_algorithm_update': 0.0011304857534006102, 'loss': nan, 'time_step': 0.0011736056126585793, 'td_error': nan} step=218\n", + " From worker 3:\t2023-06-21 09:17:48 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091748/model_218.pt\n", + " From worker 3:\t2023-06-21 09:17:48 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:49 [info ] Directory is created at d3rlpy_logs/DQN_20230621091749\n", + " From worker 2:\t2023-06-21 09:17:49 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:49 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:49 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:49 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091749/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.2017066 , 3.8068576 , 3.1260934 , 4.35434 , 2.5534215 ,\n", + " From worker 2:\t 1.2042079 , 0.32398468]], dtype=float32), 'minimum': array([[-2.1025314 , -2.9232402 , -2.7936583 , -2.3866355 , -3.2439196 ,\n", + " From worker 2:\t -1.6064517 , -0.23013951]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|████████████████| 218/218 [00:00<00:00, 840.38it/s, loss=0.118]\n", + " From worker 2:\t2023-06-21 09:17:49 [info ] DQN_20230621091749: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.6543206031169366e-05, 'time_algorithm_update': 0.0011419735917257607, 'loss': 0.1153688499310968, 'time_step': 0.001184966586051731, 'td_error': 0.34403533425650623} step=218\n", + " From worker 2:\t2023-06-21 09:17:49 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091749/model_218.pt\n", + " From worker 2:\t2023-06-21 09:17:49 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:50 [info ] Directory is created at d3rlpy_logs/DQN_20230621091750\n", + " From worker 3:\t2023-06-21 09:17:50 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:50 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:50 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:50 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091750/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32), 'minimum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|██████████████████| 190/190 [00:00<00:00, 840.56it/s, loss=nan]\n", + " From worker 3:\t2023-06-21 09:17:50 [info ] DQN_20230621091750: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 2.566764229222348e-05, 'time_algorithm_update': 0.001141886962087531, 'loss': nan, 'time_step': 0.0011843317433407432, 'td_error': nan} step=190\n", + " From worker 3:\t2023-06-21 09:17:50 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091750/model_190.pt\n", + " From worker 3:\t2023-06-21 09:17:50 [debug ] RoundIterator is selected.\n", + " From worker 5:\t2023-06-21 09:17:51 [info ] Directory is created at d3rlpy_logs/DQN_20230621091751\n", + " From worker 5:\t2023-06-21 09:17:51 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 5:\t2023-06-21 09:17:51 [debug ] Building models...\n", + " From worker 5:\t2023-06-21 09:17:51 [debug ] Models have been built.\n", + " From worker 5:\t2023-06-21 09:17:51 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091751/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.315809 , 0.7300549 , 0.8098967 , 1.2654152 , 0.7491175 ,\n", + " From worker 5:\t 0.5436357 , 0.20047979]], dtype=float32), 'minimum': array([[-0.5729552 , -0.7144378 , -1.327884 , -0.81232125, -0.81751764,\n", + " From worker 5:\t -0.5665671 , -0.28309873]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 852.55it/s, loss=0.0784]\n", + " From worker 5:\t2023-06-21 09:17:52 [info ] DQN_20230621091751: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.64316523840668e-05, 'time_algorithm_update': 0.0011246554348446906, 'loss': 0.0772749390623985, 'time_step': 0.0011681132360335884, 'td_error': 0.3391844592173055} step=218\n", + " From worker 5:\t2023-06-21 09:17:52 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091751/model_218.pt\n", + " From worker 5:\t2023-06-21 09:17:52 [debug ] RoundIterator is selected.\n", + " From worker 5:\t2023-06-21 09:17:52 [info ] Directory is created at d3rlpy_logs/DQN_20230621091752\n", + " From worker 5:\t2023-06-21 09:17:52 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 5:\t2023-06-21 09:17:52 [debug ] Building models...\n", + " From worker 5:\t2023-06-21 09:17:52 [debug ] Models have been built.\n", + " From worker 5:\t2023-06-21 09:17:52 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091752/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.315809 , 0.7300549 , 0.8098967 , 1.2654152 , 0.7321317 ,\n", + " From worker 5:\t 0.5388433 , 0.20047979]], dtype=float32), 'minimum': array([[-0.5729552 , -0.7144378 , -1.327884 , -0.81232125, -0.81751764,\n", + " From worker 5:\t -0.5665671 , -0.28309873]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 914.30it/s, loss=0.0887]\n", + " From worker 5:\t2023-06-21 09:17:52 [info ] DQN_20230621091752: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.3355177783091135e-05, 'time_algorithm_update': 0.0010528805059030516, 'loss': 0.08727689919600246, 'time_step': 0.0010901317683928605, 'td_error': 0.33895740038788924} step=218\n", + " From worker 5:\t2023-06-21 09:17:52 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091752/model_218.pt\n", + " From worker 5:\t2023-06-21 09:17:52 [debug ] RoundIterator is selected.\n", + " From worker 5:\t2023-06-21 09:17:53 [info ] Directory is created at d3rlpy_logs/DQN_20230621091753\n", + " From worker 5:\t2023-06-21 09:17:53 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 5:\t2023-06-21 09:17:53 [debug ] Building models...\n", + " From worker 5:\t2023-06-21 09:17:53 [debug ] Models have been built.\n", + " From worker 5:\t2023-06-21 09:17:53 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091753/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.315809 , 0.7300549 , 0.8098967 , 1.2654152 , 0.7491175 ,\n", + " From worker 5:\t 0.5436357 , 0.20047979]], dtype=float32), 'minimum': array([[-0.5729552 , -0.7144378 , -1.327884 , -0.81232125, -0.81751764,\n", + " From worker 5:\t -0.5665671 , -0.28309873]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 809.84it/s, loss=0.0817]\n", + " From worker 5:\t2023-06-21 09:17:53 [info ] DQN_20230621091753: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.8666006315738784e-05, 'time_algorithm_update': 0.001183026427522712, 'loss': 0.08032481709854045, 'time_step': 0.001229493989856965, 'td_error': 0.3359769608276147} step=218\n", + " From worker 5:\t2023-06-21 09:17:53 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091753/model_218.pt\n", + " From worker 7:\t2023-06-21 09:17:53 [debug ] RoundIterator is selected.\n", + " From worker 7:\t2023-06-21 09:17:53 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091753\n", + " From worker 7:\t2023-06-21 09:17:53 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 7:\t2023-06-21 09:17:53 [debug ] Building models...\n", + " From worker 7:\t2023-06-21 09:17:53 [debug ] Models have been built.\n", + " From worker 7:\t2023-06-21 09:17:53 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091753/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[249.11354 , 268.5594 , 98.88546 , 30.186954, 10.7589 ,\n", + " From worker 7:\t 100.21267 , 29.41048 ]], dtype=float32), 'minimum': array([[-250.33273 , -273.6624 , -50.02649 , -29.866972, -12.57127 ,\n", + " From worker 7:\t -98.42987 , -29.84362 ]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█| 123/123 [00:00<00:00, 242.14it/s, temp_loss=-.601, temp=1, c\n", + " From worker 7:\t2023-06-21 09:17:54 [info ] DiscreteSAC_20230621091753: epoch=1 step=123 epoch=1 metrics={'time_sample_batch': 5.082773968456237e-05, 'time_algorithm_update': 0.00404701581815394, 'temp_loss': -0.6160907208828664, 'temp': 1.004140782162426, 'critic_loss': 4.586637864267923, 'actor_loss': -4.934140949714474, 'time_step': 0.004121117475556164, 'td_error': 2.400220012705727} step=123\n", + " From worker 7:\t2023-06-21 09:17:54 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091753/model_123.pt\n", + " From worker 7:\t2023-06-21 09:17:54 [debug ] RoundIterator is selected.\n", + " From worker 7:\t2023-06-21 09:17:54 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091754\n", + " From worker 7:\t2023-06-21 09:17:54 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 7:\t2023-06-21 09:17:54 [debug ] Building models...\n", + " From worker 7:\t2023-06-21 09:17:54 [debug ] Models have been built.\n", + " From worker 7:\t2023-06-21 09:17:54 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091754/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[249.43936 , 270.23843 , 50.170696, 30.186954, 10.7589 ,\n", + " From worker 7:\t 100.21267 , 29.744823]], dtype=float32), 'minimum': array([[-250.33273 , -274.04565 , -49.614845, -29.988697, -9.468852,\n", + " From worker 7:\t -99.13402 , -30.205732]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 2:\t2023-06-21 09:17:54 [info ] Directory is created at d3rlpy_logs/DQN_20230621091754\n", + " From worker 2:\t2023-06-21 09:17:54 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:54 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:54 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:54 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091754/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.2017066 , 3.8068576 , 3.1260934 , 4.35434 , 2.5534215 ,\n", + " From worker 2:\t 1.3774092 , 0.32658172]], dtype=float32), 'minimum': array([[-2.1025314 , -2.9232402 , -2.7936583 , -2.3866355 , -3.2439196 ,\n", + " From worker 2:\t -1.4053072 , -0.32029364]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|█| 95/95 [00:00<00:00, 231.40it/s, temp_loss=-.711, temp=1, cri\n", + " From worker 7:\t2023-06-21 09:17:55 [info ] DiscreteSAC_20230621091754: epoch=1 step=95 epoch=1 metrics={'time_sample_batch': 5.0424274645353616e-05, 'time_algorithm_update': 0.0042391827231959296, 'temp_loss': -0.7389284298853263, 'temp': 1.0043758367237292, 'critic_loss': 4.360287598559731, 'actor_loss': -4.9657077889693415, 'time_step': 0.004313064876355623, 'td_error': 0.6025613924128718} step=95\n", + " From worker 7:\t2023-06-21 09:17:55 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091754/model_95.pt\n", + " From worker 7:\t2023-06-21 09:17:55 [debug ] RoundIterator is selected.\n", + " From worker 7:\t2023-06-21 09:17:55 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091755\n", + " From worker 7:\t2023-06-21 09:17:55 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 7:\t2023-06-21 09:17:55 [debug ] Building models...\n", + " From worker 7:\t2023-06-21 09:17:55 [debug ] Models have been built.\n", + " From worker 7:\t2023-06-21 09:17:55 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091755/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[249.11354 , 268.5594 , 98.88546 , 30.186954, 10.7589 ,\n", + " From worker 7:\t 100.21267 , 29.41048 ]], dtype=float32), 'minimum': array([[-250.33273 , -273.6624 , -50.02649 , -29.866972, -12.57127 ,\n", + " From worker 7:\t -98.42987 , -29.84362 ]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████| 246/246 [00:00<00:00, 791.98it/s, loss=0.0905]\n", + " From worker 2:\t2023-06-21 09:17:55 [info ] DQN_20230621091754: epoch=1 step=246 epoch=1 metrics={'time_sample_batch': 2.9921531677246094e-05, 'time_algorithm_update': 0.0012090118919930806, 'loss': 0.08958895225077868, 'time_step': 0.0012574554458866275, 'td_error': 0.3250261947255372} step=246\n", + " From worker 2:\t2023-06-21 09:17:55 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091754/model_246.pt\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:542: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n", + " From worker 2:\t warnings.warn(\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:123: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n", + " From worker 2:\t warnings.warn(\n", + " From worker 2:\t2023-06-21 09:17:55 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:55 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091755\n", + " From worker 2:\t2023-06-21 09:17:55 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:55 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:55 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:55 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091755/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.72402688e+11, 2.24971407e+11, 1.02857015e+12, 3.55687046e+12,\n", + " From worker 2:\t 5.37091244e+11, 6.58568276e+12, 1.70976110e+12]], dtype=float32), 'minimum': array([[-2.3231450e-02, -4.6398390e+11, -3.0836313e+11, -7.4163159e+11,\n", + " From worker 2:\t -1.2675402e+12, -2.5034414e+12, -5.7268870e+12]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████████| 78/78 [00:00<00:00, 866.27it/s, loss=2.27]\n", + " From worker 2:\terror in DiscreteBC2023-06-21 09:17:55 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|█| 123/123 [00:00<00:00, 217.74it/s, temp_loss=-.676, temp=1.01\n", + " From worker 7:\t2023-06-21 09:17:55 [info ] DiscreteSAC_20230621091755: epoch=1 step=123 epoch=1 metrics={'time_sample_batch': 5.268469089415015e-05, 'time_algorithm_update': 0.004506025857072536, 'temp_loss': -0.684950694621624, 'temp': 1.0064082373448504, 'critic_loss': 4.238984786398042, 'actor_loss': -4.9216519371280825, 'time_step': 0.004583397531897072, 'td_error': 2.007497995643337} step=123\n", + " From worker 7:\t2023-06-21 09:17:55 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091755/model_123.pt\n", + " From worker 3:\t2023-06-21 09:17:55 [info ] Directory is created at d3rlpy_logs/DQN_20230621091755\n", + " From worker 3:\t2023-06-21 09:17:55 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:55 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:55 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:55 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091755/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32), 'minimum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|██████████████████| 218/218 [00:00<00:00, 843.91it/s, loss=nan]\n", + " From worker 3:\t2023-06-21 09:17:56 [info ] DQN_20230621091755: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.6251197954930297e-05, 'time_algorithm_update': 0.0011368268126741461, 'loss': nan, 'time_step': 0.001179718096321876, 'td_error': nan} step=218\n", + " From worker 3:\t2023-06-21 09:17:56 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091755/model_218.pt\n", + " From worker 3:\t2023-06-21 09:17:56 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:56 [info ] Directory is created at d3rlpy_logs/DQN_20230621091756\n", + " From worker 3:\t2023-06-21 09:17:56 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:56 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:56 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:56 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091756/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9306842 , 1.1401778 , 0.726072 , 0.33919048, 0.40193763,\n", + " From worker 3:\t 0.2281029 , 0.7055056 ]], dtype=float32), 'minimum': array([[-0.46761814, -0.36881566, -0.31744727, -0.32251614, -0.45945558,\n", + " From worker 3:\t -0.46688473, -0.53166074]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 877.64it/s, loss=0.0968]\n", + " From worker 3:\t2023-06-21 09:17:56 [info ] DQN_20230621091756: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.6084961147483336e-05, 'time_algorithm_update': 0.001093139342211802, 'loss': 0.0951381272918314, 'time_step': 0.0011351053867865047, 'td_error': 0.3471383628173835} step=218\n", + " From worker 3:\t2023-06-21 09:17:56 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091756/model_218.pt\n", + " From worker 3:\t2023-06-21 09:17:56 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:56 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091756\n", + " From worker 2:\t2023-06-21 09:17:56 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:56 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:56 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:56 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091756/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.2017066, 2.9570794, 2.6705673, 3.0438037, 3.5316846, 1.699613 ,\n", + " From worker 2:\t 1.0649518]], dtype=float32), 'minimum': array([[-1.6466842, -4.368038 , -3.1260934, -3.867478 , -2.722415 ,\n", + " From worker 2:\t -2.443797 , -2.5203583]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████████| 78/78 [00:00<00:00, 939.07it/s, loss=2.21]\n", + " From worker 2:\t\n", + " From worker 2:\terror in DiscreteBC\n", + " From worker 2:\t2023-06-21 09:17:56 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:56 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091756\n", + " From worker 2:\t2023-06-21 09:17:56 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:56 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:56 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:56 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091756/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.2017066, 4.312921 , 2.968088 , 4.3629656, 3.9302883, 2.4414213,\n", + " From worker 2:\t 1.0649518]], dtype=float32), 'minimum': array([[-2.1025314, -2.7067137, -2.7774625, -2.4111278, -3.2439196,\n", + " From worker 2:\t -1.6182172, -2.5203583]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█| 109/109 [00:00<00:00, 256.12it/s, temp_loss=-.589, temp=0.99\n", + " From worker 2:\t2023-06-21 09:17:57 [info ] DiscreteSAC_20230621091756: epoch=1 step=109 epoch=1 metrics={'time_sample_batch': 4.952325733429795e-05, 'time_algorithm_update': 0.0038246167909114733, 'temp_loss': -0.5941504532729762, 'temp': 0.995188945477162, 'critic_loss': 4.329673581167099, 'actor_loss': -4.933959532221523, 'time_step': 0.003896135802662701, 'td_error': 0.46266440524758845} step=109\n", + " From worker 2:\t2023-06-21 09:17:57 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091756/model_109.pt\n", + " From worker 2:\t2023-06-21 09:17:57 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:57 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091757\n", + " From worker 2:\t2023-06-21 09:17:57 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:57 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:57 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:57 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091757/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.2017066, 4.312921 , 2.968088 , 4.3629656, 3.9302883, 2.4414213,\n", + " From worker 2:\t 1.0649518]], dtype=float32), 'minimum': array([[-2.1025314, -2.7067137, -2.7729053, -2.4111278, -3.2439196,\n", + " From worker 2:\t -1.6182172, -2.5203583]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 3:\t2023-06-21 09:17:57 [info ] Directory is created at d3rlpy_logs/DQN_20230621091757\n", + " From worker 3:\t2023-06-21 09:17:57 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:57 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:57 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:57 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091757/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9306842 , 1.0696301 , 0.726072 , 0.32656038, 0.40193763,\n", + " From worker 3:\t 0.2281029 , 0.7055056 ]], dtype=float32), 'minimum': array([[-0.46761814, -0.36881566, -0.31744727, -0.32251614, -0.45945558,\n", + " From worker 3:\t -0.46688473, -0.53166074]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 698.20it/s, loss=0.0834]\n", + "Epoch 1/1: 100%|█| 109/109 [00:00<00:00, 213.08it/s, temp_loss=-.588, temp=0.99\n", + " From worker 3:\t2023-06-21 09:17:57 [info ] DQN_20230621091757: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 3.408729483228211e-05, 'time_algorithm_update': 0.0013705765435455043, 'loss': 0.082053680357378, 'time_step': 0.0014254740618784493, 'td_error': 0.3401573140539434} step=218\n", + " From worker 3:\t2023-06-21 09:17:57 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091757/model_218.pt\n", + " From worker 3:\t2023-06-21 09:17:57 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:17:57 [info ] DiscreteSAC_20230621091757: epoch=1 step=109 epoch=1 metrics={'time_sample_batch': 5.6034928068108514e-05, 'time_algorithm_update': 0.004601797926316567, 'temp_loss': -0.5967188704034758, 'temp': 0.9934305532262959, 'critic_loss': 4.554059888244769, 'actor_loss': -4.885921320783982, 'time_step': 0.004683912347216125, 'td_error': 0.41095896724406156} step=109\n", + " From worker 2:\t2023-06-21 09:17:57 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091757/model_109.pt\n", + " From worker 2:\t2023-06-21 09:17:57 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:58 [info ] Directory is created at d3rlpy_logs/DQN_20230621091758\n", + " From worker 3:\t2023-06-21 09:17:58 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:17:58 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091758\n", + " From worker 2:\t2023-06-21 09:17:58 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:58 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:58 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:58 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091758/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9306842 , 1.1401778 , 0.726072 , 0.33919048, 0.39953673,\n", + " From worker 3:\t 0.22750928, 0.8964623 ]], dtype=float32), 'minimum': array([[-0.46761814, -0.37498793, -0.32135844, -0.2982687 , -0.50954545,\n", + " From worker 3:\t -0.45328155, -0.5060656 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 2:\t2023-06-21 09:17:58 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:17:58 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:17:58 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091758/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[3.2017066, 4.312921 , 3.0104208, 4.3629656, 3.9302883, 2.4414213,\n", + " From worker 2:\t 1.0649518]], dtype=float32), 'minimum': array([[-2.1025314, -2.7067137, -2.941255 , -2.3866355, -3.2439196,\n", + " From worker 2:\t -1.6358759, -2.5203583]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 712.20it/s, loss=0.0949]\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] DQN_20230621091758: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 3.299909994142865e-05, 'time_algorithm_update': 0.00134372929914282, 'loss': 0.09326398391447482, 'time_step': 0.0013967500914127454, 'td_error': 0.3534912199521699} step=218\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091758/model_218.pt\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091759\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091759/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1.8103775, 0. , 0. , 0. , 1.6644697, 0. ,\n", + " From worker 3:\t 0.2135467]], dtype=float32), 'minimum': array([[-0.52864933, 0. , 0. , 0. , -0.9911892 ,\n", + " From worker 3:\t 0. , -0.21503155]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|████████████████████| 70/70 [00:00<00:00, 841.43it/s, loss=nan]\n", + " From worker 3:\terror in DiscreteBC2023-06-21 09:17:59 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091759\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] Building models...\n", + "Epoch 1/1: 100%|█| 123/123 [00:00<00:00, 222.98it/s, temp_loss=-.578, temp=0.99\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091759/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9306842 , 1.1384733 , 0.7587837 , 0.3478039 , 0.40335074,\n", + " From worker 3:\t 0.2281029 , 0.7128955 ]], dtype=float32), 'minimum': array([[-0.46346295, -0.37549204, -0.45391178, -0.4061369 , -0.50954545,\n", + " From worker 3:\t -0.47077656, -0.5478712 ]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 2:\t2023-06-21 09:17:59 [info ] DiscreteSAC_20230621091758: epoch=1 step=123 epoch=1 metrics={'time_sample_batch': 5.458428607723577e-05, 'time_algorithm_update': 0.0043946533668331985, 'temp_loss': -0.5776027046537738, 'temp': 0.9948140082320547, 'critic_loss': 4.265293878753011, 'actor_loss': -4.916958991104995, 'time_step': 0.004474630200765967, 'td_error': 0.3660259082690154} step=123\n", + " From worker 2:\t2023-06-21 09:17:59 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091758/model_123.pt\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:542: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n", + " From worker 2:\t warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:123: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n", + " From worker 2:\t warnings.warn(\n", + " From worker 2:\t2023-06-21 09:17:59 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|█| 95/95 [00:00<00:00, 226.11it/s, temp_loss=-.558, temp=0.991,\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] DiscreteSAC_20230621091759: epoch=1 step=95 epoch=1 metrics={'time_sample_batch': 5.3493600142629526e-05, 'time_algorithm_update': 0.004334371968319542, 'temp_loss': -0.6238991316726529, 'temp': 0.9914160069666411, 'critic_loss': 4.550972572753304, 'actor_loss': -4.8012980159960295, 'time_step': 0.004412668629696494, 'td_error': 0.3674936336468447} step=95\n", + " From worker 3:\t2023-06-21 09:17:59 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091759/model_95.pt\n", + " From worker 3:\t2023-06-21 09:17:59 [debug ] RoundIterator is selected.\n", + " From worker 6:\t2023-06-21 09:17:59 [info ] Directory is created at d3rlpy_logs/DQN_20230621091759\n", + " From worker 6:\t2023-06-21 09:17:59 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 6:\t2023-06-21 09:17:59 [debug ] Building models...\n", + " From worker 6:\t2023-06-21 09:17:59 [debug ] Models have been built.\n", + " From worker 6:\t2023-06-21 09:17:59 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091759/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[249.77428 , 271.3161 , 50.14434 , 30.42416 , 10.7589 ,\n", + " From worker 6:\t 100.1691 , 30.100527]], dtype=float32), 'minimum': array([[-249.33698 , -268.4478 , -49.885757, -29.988697, -11.494537,\n", + " From worker 6:\t -99.35471 , -29.723509]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|████████████████| 190/190 [00:00<00:00, 883.40it/s, loss=0.124]\n", + " From worker 6:\t2023-06-21 09:18:00 [info ] DQN_20230621091759: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 2.6126911765650698e-05, 'time_algorithm_update': 0.0010848685314780787, 'loss': 0.12158315730722327, 'time_step': 0.0011276119633724815, 'td_error': 0.4381055625556321} step=190\n", + " From worker 6:\t2023-06-21 09:18:00 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091759/model_190.pt\n", + " From worker 6:\t2023-06-21 09:18:00 [debug ] RoundIterator is selected.\n", + " From worker 6:\t2023-06-21 09:18:00 [info ] Directory is created at d3rlpy_logs/DQN_20230621091800\n", + " From worker 6:\t2023-06-21 09:18:00 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 6:\t2023-06-21 09:18:00 [debug ] Building models...\n", + " From worker 6:\t2023-06-21 09:18:00 [debug ] Models have been built.\n", + " From worker 6:\t2023-06-21 09:18:00 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091800/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[250.24196 , 271.3161 , 215.67331 , 30.42416 , 10.7589 ,\n", + " From worker 6:\t 163.16124 , 30.100527]], dtype=float32), 'minimum': array([[-249.33698 , -268.4478 , -49.885757, -29.988697, -11.494537,\n", + " From worker 6:\t -99.36066 , -163.37228 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|████████████████| 218/218 [00:00<00:00, 859.26it/s, loss=0.167]\n", + " From worker 6:\t2023-06-21 09:18:00 [info ] DQN_20230621091800: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.437228456549688e-05, 'time_algorithm_update': 0.001121238830986373, 'loss': 0.1643176573337218, 'time_step': 0.0011600376269139282, 'td_error': 0.43984013669077254} step=218\n", + " From worker 6:\t2023-06-21 09:18:00 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091800/model_218.pt\n", + " From worker 6:\t2023-06-21 09:18:00 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:18:00 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091800\n", + " From worker 2:\t2023-06-21 09:18:00 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:18:00 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:18:00 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:18:00 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091800/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[4.0430723e+12, 1.8960426e+12, 3.1853068e+12, 6.7555387e+11,\n", + " From worker 2:\t 2.3783101e+12, 5.4438857e+12, 3.8419759e+12]], dtype=float32), 'minimum': array([[-6.3648321e+11, -1.1384359e+12, -1.8285029e+12, -4.3468859e+11,\n", + " From worker 2:\t -3.0583730e+12, -5.3568445e+12, -4.1177155e+12]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█| 109/109 [00:00<00:00, 248.32it/s, temp_loss=-.586, temp=0.99\n", + " From worker 2:\t2023-06-21 09:18:01 [info ] DiscreteSAC_20230621091800: epoch=1 step=109 epoch=1 metrics={'time_sample_batch': 5.08115925920119e-05, 'time_algorithm_update': 0.003944429782552457, 'temp_loss': -0.6070170115525706, 'temp': 0.9968975908165678, 'critic_loss': 4.642771337010445, 'actor_loss': -4.992712204609442, 'time_step': 0.004018339542073941, 'td_error': 0.3385400824074033} step=109\n", + " From worker 2:\t2023-06-21 09:18:01 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091800/model_109.pt\n", + " From worker 2:\t2023-06-21 09:18:01 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:18:01 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091801\n", + " From worker 2:\t2023-06-21 09:18:01 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:18:01 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:18:01 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:18:01 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091801/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[4.0430723e+12, 1.8960426e+12, 3.1853068e+12, 6.7555387e+11,\n", + " From worker 2:\t 2.3783101e+12, 5.4438857e+12, 3.8419759e+12]], dtype=float32), 'minimum': array([[-6.3648321e+11, -1.1384359e+12, -1.1599949e+12, -2.8665627e+11,\n", + " From worker 2:\t -3.0583730e+12, -5.3568445e+12, -4.1177155e+12]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|█| 109/109 [00:00<00:00, 255.19it/s, temp_loss=-.523, temp=0.99\n", + " From worker 6:\t2023-06-21 09:18:01 [info ] Directory is created at d3rlpy_logs/DQN_20230621091801\n", + " From worker 6:\t2023-06-21 09:18:01 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 6:\t2023-06-21 09:18:01 [debug ] Building models...\n", + " From worker 6:\t2023-06-21 09:18:01 [debug ] Models have been built.\n", + " From worker 6:\t2023-06-21 09:18:01 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091801/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[250.24196 , 271.3161 , 215.67331 , 30.42416 , 10.7589 ,\n", + " From worker 6:\t 163.16124 , 30.100527]], dtype=float32), 'minimum': array([[-249.33698 , -268.4478 , -49.885757, -29.988697, -11.494537,\n", + " From worker 6:\t -99.36066 , -163.37228 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 2:\t2023-06-21 09:18:01 [info ] DiscreteSAC_20230621091801: epoch=1 step=109 epoch=1 metrics={'time_sample_batch': 4.7020955916938434e-05, 'time_algorithm_update': 0.0038419404161085776, 'temp_loss': -0.5592389937250986, 'temp': 0.9972517807549293, 'critic_loss': 4.588769269645761, 'actor_loss': -4.982084239294769, 'time_step': 0.00391071652053693, 'td_error': 0.36861823891947937} step=109\n", + " From worker 2:\t2023-06-21 09:18:01 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091801/model_109.pt\n", + " From worker 2:\t2023-06-21 09:18:01 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|████████████████| 218/218 [00:00<00:00, 897.00it/s, loss=0.121]\n", + " From worker 6:\t2023-06-21 09:18:01 [info ] DQN_20230621091801: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.3999345411948106e-05, 'time_algorithm_update': 0.0010723859891978973, 'loss': 0.1192954094106451, 'time_step': 0.00111099230040104, 'td_error': 0.41862508386107267} step=218\n", + " From worker 6:\t2023-06-21 09:18:01 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091801/model_218.pt\n", + " From worker 6:\t2023-06-21 09:18:01 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:18:02 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091802\n", + " From worker 2:\t2023-06-21 09:18:02 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:18:02 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:18:02 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:18:02 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091802/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[4.0430723e+12, 1.8960426e+12, 3.1853068e+12, 6.7555387e+11,\n", + " From worker 2:\t 2.3783101e+12, 5.4438857e+12, 3.8419759e+12]], dtype=float32), 'minimum': array([[-6.3648321e+11, -1.1384359e+12, -1.1599949e+12, -2.8665627e+11,\n", + " From worker 2:\t -3.0583730e+12, -5.3568445e+12, -4.1177155e+12]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 4:\t2023-06-21 09:18:02 [info ] Directory is created at d3rlpy_logs/DQN_20230621091802\n", + " From worker 4:\t2023-06-21 09:18:02 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:18:02 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:18:02 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:18:02 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091802/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[1. , 0.9285714, 1. , 1. , 1.0121458, 1. ,\n", + " From worker 4:\t 1. ]], dtype=float32), 'minimum': array([[ 0. , -0.71428573, -0.96666664, 0. , -1.0080972 ,\n", + " From worker 4:\t -0.99 , -0.98 ]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█| 109/109 [00:00<00:00, 241.09it/s, temp_loss=-.566, temp=0.99\n", + " From worker 2:\t2023-06-21 09:18:03 [info ] DiscreteSAC_20230621091802: epoch=1 step=109 epoch=1 metrics={'time_sample_batch': 4.8385847599134534e-05, 'time_algorithm_update': 0.004068871156884989, 'temp_loss': -0.5941836126682716, 'temp': 0.9984096682399785, 'critic_loss': 4.425358019290714, 'actor_loss': -4.960146169050025, 'time_step': 0.004139005591016297, 'td_error': 0.34059097209100414} step=109\n", + " From worker 2:\t2023-06-21 09:18:03 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091802/model_109.pt\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:542: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n", + " From worker 2:\t warnings.warn(\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 769.17it/s, loss=0.0892]\n", + " From worker 4:\t2023-06-21 09:18:03 [info ] DQN_20230621091802: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.7939813946365218e-05, 'time_algorithm_update': 0.0012496075498948404, 'loss': 0.08782375861095999, 'time_step': 0.0012951640907777559, 'td_error': 0.3477889492142646} step=218\n", + " From worker 4:\t2023-06-21 09:18:03 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091802/model_218.pt\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:123: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n", + " From worker 2:\t warnings.warn(\n", + " From worker 2:\t2023-06-21 09:18:03 [debug ] RoundIterator is selected.\n", + " From worker 2:\t2023-06-21 09:18:03 [info ] Directory is created at d3rlpy_logs/DiscreteBC_20230621091803\n", + " From worker 2:\t2023-06-21 09:18:03 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:18:03 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:18:03 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:18:03 [info ] Parameters are saved to d3rlpy_logs/DiscreteBC_20230621091803/params.json params={'action_scaler': None, 'batch_size': 100, 'beta': 0.5, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 1.0, 'generated_maxlen': 100000, 'learning_rate': 0.001, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[2.1615496e+12, 2.6355292e+12, 2.1067614e+12, 1.6548812e+11,\n", + " From worker 2:\t 4.0524309e+12, 2.4732295e+12, 1.3723012e+12]], dtype=float32), 'minimum': array([[-4.5286566e+12, -1.7273379e+12, -4.7074060e+11, -5.8335625e+11,\n", + " From worker 2:\t -3.0358634e+12, -2.8378270e+12, -1.3656961e+12]], dtype=float32)}}, 'use_gpu': None, 'algorithm': 'DiscreteBC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 4:\t2023-06-21 09:18:03 [debug ] RoundIterator is selected.\n", + " From worker 4:\t2023-06-21 09:18:03 [info ] Directory is created at d3rlpy_logs/DQN_20230621091803\n", + " From worker 4:\t2023-06-21 09:18:03 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:18:03 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:18:03 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:18:03 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091803/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9207175, 2.7306168, 2.8907115, 2.5570333, 2.8882327, 1.2662084,\n", + " From worker 4:\t 0.5209782]], dtype=float32), 'minimum': array([[-1.2842066 , -2.7194939 , -2.822502 , -2.5797536 , -2.9841356 ,\n", + " From worker 4:\t -1.2347333 , -0.38340685]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|███████████████████| 69/69 [00:00<00:00, 888.80it/s, loss=2.32]\n", + " From worker 2:\terror in DiscreteBC\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:542: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n", + " From worker 2:\t warnings.warn(\n", + "Epoch 1/1: 100%|███████████████| 190/190 [00:00<00:00, 826.76it/s, loss=0.0938]\n", + " From worker 4:\t2023-06-21 09:18:03 [info ] DQN_20230621091803: epoch=1 step=190 epoch=1 metrics={'time_sample_batch': 2.738927540026213e-05, 'time_algorithm_update': 0.0011602765635440224, 'loss': 0.09130570031702519, 'time_step': 0.0012044906616210938, 'td_error': 0.33305522559426054} step=190\n", + " From worker 4:\t2023-06-21 09:18:03 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091803/model_190.pt\n", + " From worker 4:\t2023-06-21 09:18:03 [debug ] RoundIterator is selected.\n", + " From worker 2:\t/Users/ppalmes/phome/julia/AutoMLPipeline.jl/AutoOfflineRL/examples/.CondaPkg/env/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py:123: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n", + " From worker 2:\t warnings.warn(\n", + " From worker 2:\t2023-06-21 09:18:03 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:18:03 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091803\n", + " From worker 3:\t2023-06-21 09:18:03 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:18:03 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:18:03 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:18:03 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091803/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9306842 , 1.1384733 , 0.7587837 , 0.3478039 , 0.40335074,\n", + " From worker 3:\t 0.2281029 , 0.7128955 ]], dtype=float32), 'minimum': array([[-0.46346295, -0.37549204, -0.45391178, -0.4061369 , -0.50954545,\n", + " From worker 3:\t -0.47077656, -0.5478712 ]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█| 95/95 [00:00<00:00, 233.10it/s, temp_loss=-.606, temp=0.992,\n", + " From worker 3:\t2023-06-21 09:18:04 [info ] DiscreteSAC_20230621091803: epoch=1 step=95 epoch=1 metrics={'time_sample_batch': 4.924222042686061e-05, 'time_algorithm_update': 0.004208978853727642, 'temp_loss': -0.65238654556145, 'temp': 0.992238650196477, 'critic_loss': 4.486884029915458, 'actor_loss': -4.853306855653462, 'time_step': 0.004281633778622276, 'td_error': 0.41911956456499566} step=95\n", + " From worker 3:\t2023-06-21 09:18:04 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091803/model_95.pt\n", + " From worker 3:\t2023-06-21 09:18:04 [debug ] RoundIterator is selected.\n", + " From worker 3:\t2023-06-21 09:18:04 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091804\n", + " From worker 3:\t2023-06-21 09:18:04 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:18:04 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:18:04 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:18:04 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091804/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9306842 , 1.1384733 , 0.7587837 , 0.3510912 , 0.40218955,\n", + " From worker 3:\t 0.29190326, 0.5466196 ]], dtype=float32), 'minimum': array([[-0.46346295, -0.37549204, -0.45391178, -0.3651803 , -0.50954545,\n", + " From worker 3:\t -0.45396373, -0.40756777]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 4:\t2023-06-21 09:18:04 [info ] Directory is created at d3rlpy_logs/DQN_20230621091804\n", + " From worker 4:\t2023-06-21 09:18:04 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 4:\t2023-06-21 09:18:04 [debug ] Building models...\n", + " From worker 4:\t2023-06-21 09:18:04 [debug ] Models have been built.\n", + " From worker 4:\t2023-06-21 09:18:04 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091804/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0.9207175, 2.7306168, 2.8907115, 2.5570333, 2.8882327, 1.3416436,\n", + " From worker 4:\t 0.6212866]], dtype=float32), 'minimum': array([[-1.2842066 , -2.7194939 , -2.822502 , -2.5797536 , -2.9841356 ,\n", + " From worker 4:\t -1.2347333 , -0.38340685]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 100%|█| 109/109 [00:00<00:00, 239.23it/s, temp_loss=-.663, temp=0.99\n", + " From worker 3:\t2023-06-21 09:18:04 [info ] DiscreteSAC_20230621091804: epoch=1 step=109 epoch=1 metrics={'time_sample_batch': 4.9464199521126006e-05, 'time_algorithm_update': 0.004099708084666401, 'temp_loss': -0.6689465117721963, 'temp': 0.9930887457427628, 'critic_loss': 4.150376217627744, 'actor_loss': -4.793336282082654, 'time_step': 0.004171787051979555, 'td_error': 0.39833078629579516} step=109\n", + " From worker 3:\t2023-06-21 09:18:04 [info ] Model parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091804/model_109.pt\n", + " From worker 3:\t2023-06-21 09:18:04 [debug ] RoundIterator is selected.\n", + "Epoch 1/1: 100%|███████████████| 218/218 [00:00<00:00, 792.58it/s, loss=0.0856]\n", + " From worker 4:\t2023-06-21 09:18:04 [info ] DQN_20230621091804: epoch=1 step=218 epoch=1 metrics={'time_sample_batch': 2.8987543298563827e-05, 'time_algorithm_update': 0.0012087023586308191, 'loss': 0.08417961127850987, 'time_step': 0.0012563106116898564, 'td_error': 0.3772716885831778} step=218\n", + " From worker 4:\t2023-06-21 09:18:04 [info ] Model parameters are saved to d3rlpy_logs/DQN_20230621091804/model_218.pt\n", + " From worker 4:\t2023-06-21 09:18:04 [debug ] RoundIterator is selected.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " From worker 2:\t2023-06-21 09:18:05 [info ] Directory is created at d3rlpy_logs/DQN_20230621091805\n", + " From worker 2:\t2023-06-21 09:18:05 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 2:\t2023-06-21 09:18:05 [debug ] Building models...\n", + " From worker 2:\t2023-06-21 09:18:05 [debug ] Models have been built.\n", + " From worker 2:\t2023-06-21 09:18:05 [info ] Parameters are saved to d3rlpy_logs/DQN_20230621091805/params.json params={'action_scaler': None, 'batch_size': 32, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 6.25e-05, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[5.7978133e+11, 4.5132997e+12, 2.7877811e+12, 1.9566082e+12,\n", + " From worker 2:\t 1.8710200e+12, 4.4437136e+12, 2.3416296e+12]], dtype=float32), 'minimum': array([[-1.6165357e+12, -1.6694441e+12, -8.4551401e+11, -3.8393081e+12,\n", + " From worker 2:\t -1.8710200e+12, -4.4437136e+12, -1.3556292e+12]], dtype=float32)}}, 'target_update_interval': 8000, 'use_gpu': None, 'algorithm': 'DQN', 'observation_shape': (7,), 'action_size': 101}\n", + " From worker 3:\t2023-06-21 09:18:05 [info ] Directory is created at d3rlpy_logs/DiscreteSAC_20230621091805\n", + " From worker 3:\t2023-06-21 09:18:05 [debug ] Fitting scaler... scaler=min_max\n", + " From worker 3:\t2023-06-21 09:18:05 [debug ] Building models...\n", + " From worker 3:\t2023-06-21 09:18:05 [debug ] Models have been built.\n", + " From worker 3:\t2023-06-21 09:18:05 [info ] Parameters are saved to d3rlpy_logs/DiscreteSAC_20230621091805/params.json params={'action_scaler': None, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 0.0003, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'batch_size': 64, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_temperature': 1.0, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': {'type': 'min_max', 'params': {'maximum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32), 'minimum': array([[0., 0., 0., 0., 0., 0., 0.]], dtype=float32)}}, 'target_update_interval': 8000, 'temp_learning_rate': 0.0003, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 0.0001, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'DiscreteSAC', 'observation_shape': (7,), 'action_size': 101}\n", + "Epoch 1/1: 0%| | 0/109 [00:00
86×2 DataFrame
61 rows omitted
Rowpipelinetd_error
StringFloat64
1RobustScaler |> PCA |> DiscreteCQL0.348413
2RobustScaler |> FastICA |> DiscreteCQL0.375755
3PowerTransformer |> PCA |> DiscreteCQL0.360337
4PowerTransformer |> FastICA |> DiscreteCQL0.351133
5Normalizer |> PCA |> DiscreteCQL0.358127
6Normalizer |> FastICA |> DiscreteCQL0.386176
7Normalizer |> Noop |> DiscreteCQL0.37901
8StandardScaler |> PCA |> DiscreteCQL0.386691
9StandardScaler |> Noop |> DiscreteCQL0.381613
10MinMaxScaler |> PCA |> DiscreteCQL0.36608
11MinMaxScaler |> FastICA |> DiscreteCQL0.364046
12Noop |> PCA |> DiscreteCQL0.436275
13Noop |> FastICA |> DiscreteCQL0.378103
⋮⋮⋮
75PowerTransformer |> PCA |> DQN0.341005
76PowerTransformer |> FastICA |> DQN0.330706
77PowerTransformer |> Noop |> DQN5.34366e21
78Normalizer |> PCA |> DQN0.346929
79Normalizer |> FastICA |> DQN0.332194
80Normalizer |> Noop |> DQN0.340522
81StandardScaler |> PCA |> DQN0.348759
82StandardScaler |> FastICA |> DQN0.329664
83MinMaxScaler |> PCA |> DQN0.33804
84MinMaxScaler |> FastICA |> DQN0.336319
85Noop |> PCA |> DQN0.43219
86Noop |> FactorAnalysis |> DQN2.56554e10
" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& pipeline & td\\_error\\\\\n", + "\t\\hline\n", + "\t& String & Float64\\\\\n", + "\t\\hline\n", + "\t1 & RobustScaler |> PCA |> DiscreteCQL & 0.348413 \\\\\n", + "\t2 & RobustScaler |> FastICA |> DiscreteCQL & 0.375755 \\\\\n", + "\t3 & PowerTransformer |> PCA |> DiscreteCQL & 0.360337 \\\\\n", + "\t4 & PowerTransformer |> FastICA |> DiscreteCQL & 0.351133 \\\\\n", + "\t5 & Normalizer |> PCA |> DiscreteCQL & 0.358127 \\\\\n", + "\t6 & Normalizer |> FastICA |> DiscreteCQL & 0.386176 \\\\\n", + "\t7 & Normalizer |> Noop |> DiscreteCQL & 0.37901 \\\\\n", + "\t8 & StandardScaler |> PCA |> DiscreteCQL & 0.386691 \\\\\n", + "\t9 & StandardScaler |> Noop |> DiscreteCQL & 0.381613 \\\\\n", + "\t10 & MinMaxScaler |> PCA |> DiscreteCQL & 0.36608 \\\\\n", + "\t11 & MinMaxScaler |> FastICA |> DiscreteCQL & 0.364046 \\\\\n", + "\t12 & Noop |> PCA |> DiscreteCQL & 0.436275 \\\\\n", + "\t13 & Noop |> FastICA |> DiscreteCQL & 0.378103 \\\\\n", + "\t14 & Noop |> FactorAnalysis |> DiscreteCQL & 13.7154 \\\\\n", + "\t15 & Noop |> Noop |> DiscreteCQL & 0.407878 \\\\\n", + "\t16 & RobustScaler |> PCA |> NFQ & 3.56353e25 \\\\\n", + "\t17 & RobustScaler |> FastICA |> NFQ & 0.378888 \\\\\n", + "\t18 & PowerTransformer |> PCA |> NFQ & 0.348923 \\\\\n", + "\t19 & PowerTransformer |> FastICA |> NFQ & 0.575808 \\\\\n", + "\t20 & Normalizer |> PCA |> NFQ & 0.512982 \\\\\n", + "\t21 & Normalizer |> FastICA |> NFQ & 0.41633 \\\\\n", + "\t22 & Normalizer |> Noop |> NFQ & 0.430991 \\\\\n", + "\t23 & StandardScaler |> PCA |> NFQ & 0.348243 \\\\\n", + "\t24 & StandardScaler |> FastICA |> NFQ & 0.443391 \\\\\n", + "\t25 & MinMaxScaler |> PCA |> NFQ & 0.370624 \\\\\n", + "\t26 & MinMaxScaler |> FastICA |> NFQ & 0.355514 \\\\\n", + "\t27 & Noop |> PCA |> NFQ & 1.16437 \\\\\n", + "\t28 & Noop |> FastICA |> NFQ & 0.548647 \\\\\n", + "\t29 & Noop |> FactorAnalysis |> NFQ & 2.48772e14 \\\\\n", + "\t30 & RobustScaler |> PCA |> DoubleDQN & 0.334047 \\\\\n", + "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m86×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m pipeline \u001b[0m\u001b[1m td_error \u001b[0m\n", + " │\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼────────────────────────────────────────────────\n", + " 1 │ RobustScaler |> PCA |> DiscreteC… 0.348413\n", + " 2 │ RobustScaler |> FastICA |> Discr… 0.375755\n", + " 3 │ PowerTransformer |> PCA |> Discr… 0.360337\n", + " 4 │ PowerTransformer |> FastICA |> D… 0.351133\n", + " 5 │ Normalizer |> PCA |> DiscreteCQL 0.358127\n", + " 6 │ Normalizer |> FastICA |> Discret… 0.386176\n", + " 7 │ Normalizer |> Noop |> DiscreteCQL 0.37901\n", + " 8 │ StandardScaler |> PCA |> Discret… 0.386691\n", + " 9 │ StandardScaler |> Noop |> Discre… 0.381613\n", + " 10 │ MinMaxScaler |> PCA |> DiscreteC… 0.36608\n", + " 11 │ MinMaxScaler |> FastICA |> Discr… 0.364046\n", + " ⋮ │ ⋮ ⋮\n", + " 77 │ PowerTransformer |> Noop |> DQN 5.34366e21\n", + " 78 │ Normalizer |> PCA |> DQN 0.346929\n", + " 79 │ Normalizer |> FastICA |> DQN 0.332194\n", + " 80 │ Normalizer |> Noop |> DQN 0.340522\n", + " 81 │ StandardScaler |> PCA |> DQN 0.348759\n", + " 82 │ StandardScaler |> FastICA |> DQN 0.329664\n", + " 83 │ MinMaxScaler |> PCA |> DQN 0.33804\n", + " 84 │ MinMaxScaler |> FastICA |> DQN 0.336319\n", + " 85 │ Noop |> PCA |> DQN 0.43219\n", + " 86 │ Noop |> FactorAnalysis |> DQN 2.56554e10\n", + "\u001b[36m 65 rows omitted\u001b[0m" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function pipelinesearch()\n", + " agentnames = [\"DiscreteCQL\",\"NFQ\",\"DoubleDQN\",\"DiscreteSAC\",\"DiscreteBCQ\",\"DiscreteBC\",\"DQN\"]\n", + " scalers = [rb,pt,norm,std,mx,noop]\n", + " extractors = [pca,ica,fa,noop]\n", + " dfresults = @sync @distributed (vcat) for agentname in agentnames\n", + " @distributed (vcat) for sc in scalers\n", + " @distributed (vcat) for xt in extractors\n", + " try\n", + " rlagent = DiscreteRLOffline(agentname,Dict(:runtime_args=>Dict(:n_epochs=>1)))\n", + " rlpipeline = ((numf |> sc |> xt)) |> rlagent \n", + " res = crossvalidateRL(rlpipeline,observation,action_reward_terminal)\n", + " scn = sc.name[1:end - 4]; xtn = xt.name[1:end - 4]; lrn = rlagent.name[1:end - 4]\n", + " pname = \"$scn |> $xtn |> $lrn\"\n", + " if !isnan(res)\n", + " DataFrame(pipeline=pname,td_error=res)\n", + " else\n", + " DataFrame()\n", + " end\n", + " catch e\n", + " println(\"error in $agentname\")\n", + " DataFrame()\n", + " end\n", + " end\n", + " end\n", + " end\n", + " #sort!(dfresults,:percent_action_matches,rev=true)\n", + " return dfresults\n", + "end\n", + "dftable= pipelinesearch()" + ] + }, + { + "cell_type": "markdown", + "id": "1d3e98a8", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Results" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "ed8b1175", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
86×2 DataFrame
61 rows omitted
Rowpipelinetd_error
StringFloat64
1Noop |> FastICA |> DoubleDQN0.325789
2StandardScaler |> FastICA |> DQN0.329664
3PowerTransformer |> FastICA |> DQN0.330706
4PowerTransformer |> FastICA |> DiscreteSAC0.331318
5Normalizer |> FastICA |> DQN0.332194
6PowerTransformer |> FastICA |> DiscreteBCQ0.333078
7RobustScaler |> FastICA |> DQN0.33313
8StandardScaler |> Noop |> DoubleDQN0.33318
9StandardScaler |> PCA |> DoubleDQN0.333818
10RobustScaler |> PCA |> DoubleDQN0.334047
11Normalizer |> Noop |> DiscreteBCQ0.334562
12Normalizer |> PCA |> DiscreteBCQ0.335437
13RobustScaler |> FastICA |> DoubleDQN0.336118
⋮⋮⋮
75Noop |> PCA |> NFQ1.16437
76Noop |> PCA |> DiscreteSAC1.67009
77Noop |> FactorAnalysis |> DiscreteCQL13.7154
78Noop |> FactorAnalysis |> DiscreteSAC2.26795e6
79Noop |> FactorAnalysis |> DQN2.56554e10
80Noop |> FactorAnalysis |> DoubleDQN2.69701e11
81Noop |> FactorAnalysis |> DiscreteBCQ2.61634e13
82Noop |> FactorAnalysis |> NFQ2.48772e14
83PowerTransformer |> Noop |> DQN5.34366e21
84PowerTransformer |> Noop |> DoubleDQN3.26964e22
85RobustScaler |> PCA |> NFQ3.56353e25
86RobustScaler |> PCA |> DiscreteSAC3.36113e26
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5×2 DataFrame
Rowpipelinetd_error
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1Noop |> FastICA |> DoubleDQN0.325789
2StandardScaler |> FastICA |> DQN0.329664
3PowerTransformer |> FastICA |> DQN0.330706
4PowerTransformer |> FastICA |> DiscreteSAC0.331318
5Normalizer |> FastICA |> DQN0.332194
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5×2 DataFrame
Rowpipelinetd_error
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1Noop |> FactorAnalysis |> NFQ2.48772e14
2PowerTransformer |> Noop |> DQN5.34366e21
3PowerTransformer |> Noop |> DoubleDQN3.26964e22
4RobustScaler |> PCA |> NFQ3.56353e25
5RobustScaler |> PCA |> DiscreteSAC3.36113e26
" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& pipeline & td\\_error\\\\\n", + "\t\\hline\n", + "\t& String & Float64\\\\\n", + "\t\\hline\n", + "\t1 & Noop |> FactorAnalysis |> NFQ & 2.48772e14 \\\\\n", + "\t2 & PowerTransformer |> Noop |> DQN & 5.34366e21 \\\\\n", + "\t3 & PowerTransformer |> Noop |> DoubleDQN & 3.26964e22 \\\\\n", + "\t4 & RobustScaler |> PCA |> NFQ & 3.56353e25 \\\\\n", + "\t5 & RobustScaler |> PCA |> DiscreteSAC & 3.36113e26 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m5×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m pipeline \u001b[0m\u001b[1m td_error \u001b[0m\n", + " │\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼───────────────────────────────────────────────\n", + " 1 │ Noop |> FactorAnalysis |> NFQ 2.48772e14\n", + " 2 │ PowerTransformer |> Noop |> DQN 5.34366e21\n", + " 3 │ PowerTransformer |> Noop |> Doub… 3.26964e22\n", + " 4 │ RobustScaler |> PCA |> NFQ 3.56353e25\n", + " 5 │ RobustScaler |> PCA |> DiscreteS… 3.36113e26" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "last(dftable,5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a2f079f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Julia 1.9.0", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/AutoOfflineRL/examples/presentation.slides.html b/AutoOfflineRL/examples/presentation.slides.html new file mode 100644 index 00000000..b49bd49e --- /dev/null +++ b/AutoOfflineRL/examples/presentation.slides.html @@ -0,0 +1,15548 @@ + + + + + + + + + +presentation slides + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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