diff --git a/.buildkite/pipeline.yml b/.buildkite/pipeline.yml new file mode 100644 index 00000000..4dfba512 --- /dev/null +++ b/.buildkite/pipeline.yml @@ -0,0 +1,18 @@ +common: &common + plugins: + - EmbarkStudios/k8s#1.2.10: + service-account-name: monorepo-ci + image: gcr.io/embark-shared/ml/monorepo-controller@sha256:aad8ab820105ea1c0dea9dad53b1d1853b92eee93a4a7e3663fe0b265806fa8c + default-secret-name: buildkite-k8s-plugin + always-pull: true + + agents: + cluster: builds-fi-2 + queue: monorepo-ci + size: small + +steps: + - label: 📚 Publish docs + command: bash .buildkite/publish-docs.sh + branches: "main" + << : *common diff --git a/.buildkite/publish-docs.sh b/.buildkite/publish-docs.sh new file mode 100644 index 00000000..fa5d721c --- /dev/null +++ b/.buildkite/publish-docs.sh @@ -0,0 +1,39 @@ +set -eo pipefail + +echo --- Installing dependencies + +apt-get update \ + && apt-get install --no-install-recommends -y \ + curl \ + build-essential + +export PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=off \ + PIP_DISABLE_PIP_VERSION_CHECK=on \ + PIP_DEFAULT_TIMEOUT=100 \ + POETRY_VERSION=1.2.0b1 \ + POETRY_HOME="/tmp/poetry" \ + POETRY_VIRTUALENVS_IN_PROJECT=true \ + POETRY_NO_INTERACTION=1 \ + +pip install poetry==1.2.0b1 +poetry install +poetry env info --path + + +echo --- Initializing gcloud + +curl https://sdk.cloud.google.com > install.sh && \ + bash install.sh --disable-prompts 2>&1 && \ + /root/google-cloud-sdk/install.sh --path-update true --usage-reporting false --quiet + +if [ -f '/root/google-cloud-sdk/path.bash.inc' ]; then . '/root/google-cloud-sdk/path.bash.inc'; fi +gcloud config set account monorepo-ci@embark-builds.iam.gserviceaccount.com + +echo --- Building docs +pushd docs +poetry env info --path +make deploy +gsutil rsync -r ./_build/dirhtml gs://embark-static/emote-docs +popd diff --git a/.gitignore b/.gitignore index db807395..3a9f3206 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,17 @@ -__pycache__/ -.vscode -.dir-locals.el -*.egg-info -*.onnx -.coverage -docs/_build -runs/** +# Miscellaneous editor files +.vscode +.dir-locals.el + +# Output from Python and Python tools +__pycache__/ +*.egg-info +.coverage + +# Files generated by the docs publishing systems +docs/_build +docs/generated +docs/coverage.rst + +# Outputs from Emote and tests +*.onnx +runs/** diff --git a/README.md b/README.md index 1318f57d..d87c3710 100644 --- a/README.md +++ b/README.md @@ -1,141 +1,144 @@ - - - - - - -
- -# `🍒 emote` - -**E**mbark's **Mo**dular **T**raining **E**ngine - a flexible framework for -reinforcement learning - -[![Embark](https://img.shields.io/badge/embark-open%20source-blueviolet.svg)](https://embark.dev) -[![Embark](https://img.shields.io/badge/discord-ark-%237289da.svg?logo=discord)](https://discord.gg/dAuKfZS) -[![Documentation Status](https://readthedocs.org/projects/emote/badge/?version=latest)](http://emote.readthedocs.io/?badge=latest) -[![PyPI version fury.io](https://badge.fury.io/py/emote.svg)](https://pypi.python.org/pypi/emote/) -[![Build status](https://github.com/EmbarkStudios/emote/workflows/CI/badge.svg)](https://github.com/EmbarkStudios/emote/actions) - -🚧 This project is very much **work in progress and not yet ready for production use.** 🚧 - -
- - -## What it does - -Emote provides a way to build reusable components for creating reinforcement learning algorithms, and a -library of premade componenents built in this way. It is strongly inspired by the callback setup used -by Keras and FastAI. - -As an example, let us see how the SAC, the Soft Actor Critic algorithm by -[Haarnoja et al.](https://arxiv.org/abs/1801.01290) can be written using Emote. The main algorithm in -SAC is given in [Soft Actor-Critic Algorithms and Applications](https://arxiv.org/abs/1812.05905) and -looks like this: - -
- -![Main SAC algorithm](./docs/haarnoja_sac.png) - -
- -Using the components provided with Emote, we can write this as - -```python -env = DictGymWrapper(AsyncVectorEnv(10 * [HitTheMiddle])) -table = DictObsTable(spaces=env.dict_space, maxlen=1000) -memory_proxy = TableMemoryProxy(table) -dataloader = MemoryLoader(table, 100, 2, "batch_size") - -q1 = QNet(2, 1) -q2 = QNet(2, 1) -policy = Policy(2, 1) -ln_alpha = torch.tensor(1.0, requires_grad=True) -agent_proxy = FeatureAgentProxy(policy) - -callbacks = [ - QLoss(name="q1", q=q1, opt=Adam(q1.parameters(), lr=8e-3)), - QLoss(name="q2", q=q2, opt=Adam(q2.parameters(), lr=8e-3)), - PolicyLoss(pi=policy, ln_alpha=ln_alpha, q=q1, opt=Adam(policy.parameters())), - AlphaLoss(pi=policy, ln_alpha=ln_alpha, opt=Adam([ln_alpha]), n_actions=1), - QTarget(pi=policy, ln_alpha=ln_alpha, q1=q1, q2=q2), - SimpleGymCollector(env, agent_proxy, memory_proxy, warmup_steps=500), - FinalLossTestCheck([logged_cbs[2]], [10.0], 2000), -] - -trainer = Trainer(callbacks, dataloader) -trainer.train() -``` - -Here each callback in the `callbacks` list is its own reusable class that can readily be used -for other similar algorithms. The callback classes themselves are very straight forward to write. -As an example, here is the `PolicyLoss` callback. - -```python -class PolicyLoss(LossCallback): - def __init__( - self, - *, - pi: nn.Module, - ln_alpha: torch.tensor, - q: nn.Module, - opt: optim.Optimizer, - max_grad_norm: float = 10.0, - name: str = "policy", - data_group: str = "default", - ): - super().__init__( - name=name, - optimizer=opt, - network=pi, - max_grad_norm=max_grad_norm, - data_group=data_group, - ) - self.policy = pi - self._ln_alpha = ln_alpha - self.q1 = q - self.q2 = q2 - - def loss(self, observation): - p_sample, logp_pi = self.policy(**observation) - q_pi_min = self.q1(p_sample, **observation) - # using reparameterization trick - alpha = torch.exp(self._ln_alpha).detach() - policy_loss = alpha * logp_pi - q_pi_min - policy_loss = torch.mean(policy_loss) - assert policy_loss.dim() == 0 - return policy_loss -``` - -## Installation - -For installation and environment handling we use `conda`. Install it from [here](https://docs.anaconda.com/anaconda/install/). After `conda` is set up, set up and activate the emote environment by running - -```bash -conda env create -f environment.yml -conda activate emote -pip install -r pip-requirements.txt -``` - - -## Contribution - -[![Contributor Covenant](https://img.shields.io/badge/contributor%20covenant-v1.4-ff69b4.svg)](../main/CODE_OF_CONDUCT.md) - -We welcome community contributions to this project. - -Please read our [Contributor Guide](CONTRIBUTING.md) for more information on how to get started. -Please also read our [Contributor Terms](CONTRIBUTING.md#contributor-terms) before you make any contributions. - -Any contribution intentionally submitted for inclusion in an Embark Studios project, shall comply with the Rust standard licensing model (MIT OR Apache 2.0) and therefore be dual licensed as described below, without any additional terms or conditions: - -### License - -This contribution is dual licensed under EITHER OF - -* Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or ) -* MIT license ([LICENSE-MIT](LICENSE-MIT) or ) - -at your option. - -For clarity, "your" refers to Embark or any other licensee/user of the contribution. + + + + + + +
+ +# `🍒 emote` + +**E**mbark's **Mo**dular **T**raining **E**ngine - a flexible framework for +reinforcement learning + +[![Embark](https://img.shields.io/badge/embark-open%20source-blueviolet.svg)](https://embark.dev) +[![Embark](https://img.shields.io/badge/discord-ark-%237289da.svg?logo=discord)](https://discord.gg/dAuKfZS) +[![Build status](https://badge.buildkite.com/968ac3c0bb075fb878f9f973ed91406c8b257b0f050c197542.svg?theme=github&branch=ts/docs-poetry)](https://buildkite.com/embark-studios/emote) +[![Docs status](https://img.shields.io/badge/Docs-latest-brightgreen)](https://static.embark.net/emote-docs/) + +🚧 This project is very much **work in progress and not yet ready for production use.** 🚧 + +
+ + +## What it does + +Emote provides a way to build reusable components for creating reinforcement learning algorithms, and a +library of premade componenents built in this way. It is strongly inspired by the callback setup used +by Keras and FastAI. + +As an example, let us see how the SAC, the Soft Actor Critic algorithm by +[Haarnoja et al.](https://arxiv.org/abs/1801.01290) can be written using Emote. The main algorithm in +SAC is given in [Soft Actor-Critic Algorithms and Applications](https://arxiv.org/abs/1812.05905) and +looks like this: + +
+ +![Main SAC algorithm](./docs/haarnoja_sac.png) + +
+ +Using the components provided with Emote, we can write this as + +```python +env = DictGymWrapper(AsyncVectorEnv(10 * [HitTheMiddle])) +table = DictObsTable(spaces=env.dict_space, maxlen=1000) +memory_proxy = TableMemoryProxy(table) +dataloader = MemoryLoader(table, 100, 2, "batch_size") + +q1 = QNet(2, 1) +q2 = QNet(2, 1) +policy = Policy(2, 1) +ln_alpha = torch.tensor(1.0, requires_grad=True) +agent_proxy = FeatureAgentProxy(policy) + +callbacks = [ + QLoss(name="q1", q=q1, opt=Adam(q1.parameters(), lr=8e-3)), + QLoss(name="q2", q=q2, opt=Adam(q2.parameters(), lr=8e-3)), + PolicyLoss(pi=policy, ln_alpha=ln_alpha, q=q1, opt=Adam(policy.parameters())), + AlphaLoss(pi=policy, ln_alpha=ln_alpha, opt=Adam([ln_alpha]), n_actions=1), + QTarget(pi=policy, ln_alpha=ln_alpha, q1=q1, q2=q2), + SimpleGymCollector(env, agent_proxy, memory_proxy, warmup_steps=500), + FinalLossTestCheck([logged_cbs[2]], [10.0], 2000), +] + +trainer = Trainer(callbacks, dataloader) +trainer.train() +``` + +Here each callback in the `callbacks` list is its own reusable class that can readily be used +for other similar algorithms. The callback classes themselves are very straight forward to write. +As an example, here is the `PolicyLoss` callback. + +```python +class PolicyLoss(LossCallback): + def __init__( + self, + *, + pi: nn.Module, + ln_alpha: torch.tensor, + q: nn.Module, + opt: optim.Optimizer, + max_grad_norm: float = 10.0, + name: str = "policy", + data_group: str = "default", + ): + super().__init__( + name=name, + optimizer=opt, + network=pi, + max_grad_norm=max_grad_norm, + data_group=data_group, + ) + self.policy = pi + self._ln_alpha = ln_alpha + self.q1 = q + self.q2 = q2 + + def loss(self, observation): + p_sample, logp_pi = self.policy(**observation) + q_pi_min = self.q1(p_sample, **observation) + # using reparameterization trick + alpha = torch.exp(self._ln_alpha).detach() + policy_loss = alpha * logp_pi - q_pi_min + policy_loss = torch.mean(policy_loss) + assert policy_loss.dim() == 0 + return policy_loss +``` + +## Installation + +:warning: You currently **need** to use a pre-release version of the Poetry 1.2 series. :warning: + +For installation and environment handling we use `poetry`. Install it from [here](https://python-poetry.org/). After `poetry` is set up, set up and activate the emote environment by running + + + +```bash +poetry install +``` + + + + +## Contribution + +[![Contributor Covenant](https://img.shields.io/badge/contributor%20covenant-v1.4-ff69b4.svg)](../main/CODE_OF_CONDUCT.md) + +We welcome community contributions to this project. + +Please read our [Contributor Guide](CONTRIBUTING.md) for more information on how to get started. +Please also read our [Contributor Terms](CONTRIBUTING.md#contributor-terms) before you make any contributions. + +Any contribution intentionally submitted for inclusion in an Embark Studios project, shall comply with the Rust standard licensing model (MIT OR Apache 2.0) and therefore be dual licensed as described below, without any additional terms or conditions: + +### License + +This contribution is dual licensed under EITHER OF + +* Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or ) +* MIT license ([LICENSE-MIT](LICENSE-MIT) or ) + +at your option. + +For clarity, "your" refers to Embark or any other licensee/user of the contribution. diff --git a/docs/Makefile b/docs/Makefile index d4bb2cbb..e48b965a 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -3,8 +3,8 @@ # You can set these variables from the command line, and also # from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build +SPHINXOPTS ?= +SPHINXBUILD ?= poetry run sphinx-build SOURCEDIR = . BUILDDIR = _build @@ -18,3 +18,8 @@ help: # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +deploy: + @$(SPHINXBUILD) -M coverage "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + cp "$(BUILDDIR)/coverage/python.txt" "$(SOURCEDIR)/coverage.rst" + @$(SPHINXBUILD) -M dirhtml "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst new file mode 100644 index 00000000..e7441664 --- /dev/null +++ b/docs/_templates/autosummary/class.rst @@ -0,0 +1,32 @@ +{{ objname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :members: + :show-inheritance: + :inherited-members: + + {% block methods %} + .. automethod:: __init__ + + {% if methods %} + .. rubric:: {{ _('Methods') }} + + .. autosummary:: + {% for item in methods %} + ~{{ name }}.{{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block attributes %} + {% if attributes %} + .. rubric:: {{ _('Attributes') }} + + .. autosummary:: + {% for item in attributes %} + ~{{ name }}.{{ item }} + {%- endfor %} + {% endif %} + {% endblock %} diff --git a/docs/callback.rst b/docs/callback.rst new file mode 100644 index 00000000..521c0504 --- /dev/null +++ b/docs/callback.rst @@ -0,0 +1,54 @@ +emote.callback +============== + +.. currentmodule:: emote.callback +.. automodule:: emote.callback + +In this module you'll find the callback framework used by Emote. Those +who have used FastAI before will recognize it, as it's heavily +inspired by that system - but adapted for RL and our use-cases. + +The `Callback` interface +######################## + +The callback is the core interface used to hook into the Emote framework. You can think of these as events - when the training loop starts, we'll invoke `begin_training` on all callback objects. Then we'll start a new cycle, and call :meth:`Callback.begin_cycle`. + +All in all, the flow of callbacks is like this: + +.. graphviz:: + + digraph foo { + rankdir=LR; + node [shape=rectangle,style="rounded"] + + newrank=true; + { rank=same; begin_cycle; begin_batch; } + { rank=same; end_batch; end_cycle; } + + begin_training; + subgraph cluster_cycle { + label = "while cycles left" + begin_cycle; + + subgraph cluster_batch { + label = "while batches left"; + begin_batch; + backward; + end_batch; + } + end_cycle; + } + end_training; + + begin_training -> begin_cycle -> begin_batch -> backward -> end_batch; + + end_batch -> begin_batch [constraint=no]; + end_cycle -> begin_cycle [constraint=no]; + + end_batch -> end_cycle [style=dashed] + end_cycle -> end_training [style=dashed] + } + +.. autoclass:: Callback + :members: + :member-order: bysource diff --git a/docs/conf.py b/docs/conf.py index 24b4aaa6..2f361365 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -32,9 +32,15 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ + "sphinx.ext.autosummary", "sphinx.ext.autodoc", + "sphinx.ext.coverage", + "sphinx.ext.graphviz", + "sphinx_autodoc_typehints", ] +#autodoc_mock_imports = ["dataclasses"] + # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] @@ -43,6 +49,7 @@ # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] +add_module_names = False # -- Options for HTML output ------------------------------------------------- diff --git a/docs/coverage.rst b/docs/coverage.rst new file mode 100644 index 00000000..2f1e0fc3 --- /dev/null +++ b/docs/coverage.rst @@ -0,0 +1,5 @@ +Undocumented Python objects +=========================== + +To generate this file, run `make deploy`! Do not check it in. + diff --git a/docs/emote.rst b/docs/emote.rst new file mode 100644 index 00000000..bd7fe536 --- /dev/null +++ b/docs/emote.rst @@ -0,0 +1,6 @@ +emote +====== + +.. automodule:: emote +.. currentmodule:: emote + diff --git a/docs/index.rst b/docs/index.rst index 64b7281d..33bf0d7b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,6 +7,9 @@ for reinforcement learning written at Embark. Installation ============ + +.. warning:: You'll need to use a pre-release Poetry 1.2 version, e.g. 1.2.0a1 or later. Older versions will crash while installing our dependencies. + Install `Poetry `_ following the instructions on the Poetry site. Then install the package using :: @@ -21,16 +24,30 @@ sense that we could easily swap the algorithm we used and how data was collected but also in the sense that the different parts of various algorithms could be reused to build other algorithms. +.. automodule:: emote + + .. toctree:: :maxdepth: 2 - :caption: Contents: + :caption: Design + :hidden: -.. automodule:: emote - :members: + self + +.. toctree:: + :maxdepth: 6 + :caption: API Documentation + :hidden: + emote + memory + callback + +.. toctree:: + :caption: Extras + :hidden: -Indices and tables -================== + Undocumented code * :ref:`genindex` * :ref:`modindex` diff --git a/docs/memory.rst b/docs/memory.rst new file mode 100644 index 00000000..f7b78f2f --- /dev/null +++ b/docs/memory.rst @@ -0,0 +1,117 @@ +emote.memory +============ + +.. currentmodule:: emote.memory +.. automodule:: emote.memory + +This module contains all the major building blocks for our memory +implementation. The memory was developed in the same time period as +`DeepMind's Reverb `_, and shares +naming with it, which in turn is borrowing from databases. What is not +alike Reverb is that we do not have the RateSamplers (but it can be +added). We also do not share data between ArrayTables. + +The goal of the memory is to provide a unified interface for all types of +machine learning tasks. This is achieved by focusing on configuration and +pluggability over code-driven functionality. + +Currently, there are three main points of customization: + +* Shape and type of data +* Insertion, sampling, and eviction +* Data transformation and generation + +High-level parts +---------------- + +ArrayTable +########## + +A table is a datastructure containing a specific type of data that shares the same high-level structure. + +.. autosummary:: + :toctree: generated + + Table + +Columns and Virtual Columns +###### + +A column is a storage for a specific type of data where each item is +the same shape and type. A virtual column is like a column, but it +references another column and does data synthesization or modification +w.r.t that. For example, dones and masks are synthetic data based only +on indices. + +.. autosummary:: + :toctree: generated + + column.Column + column.TagColumn + column.VirtualColumn + +Adaptors +######## + +Adaptors are another approach to virtual column but are more suited for +transforming the whole batch, such as scaling for reshaping specific +datas. Since this step occurs when the data has already been converted to +tensors, the full power of Tensorflow is available here and gradients will be +correctly tracked. + +.. autosummary:: + :toctree: generated + + adaptors.DictObsAdaptor + adaptors.KeyScaleAdaptor + adaptors.KeyCastAdaptor + adaptors.KeyScaleAdaptor + adaptors.TerminalAdaptor + +Strategies, Samplers and Ejectors +################################# + +Strategies are based on the delegate pattern, where we can inject implementation +details through objects instead of using inheritance. Strategies define the API +for sampling and ejection from memories, and are queried from the table upon +sampling and insertion. + +Samplers and Ejectors track the data (but do not own it!). They are used by the +table for sampling and ejection based on the policy they implement. Currently we +have Fifo and Uniform samplers and ejectors, but one could have prioritized +samplers/ejectors, etc. + +.. topic:: Bases + + .. autosummary:: + :toctree: generated + + strategy.Strategy + strategy.SampleStrategy + strategy.EjectionStrategy + + fifo_strategy.FifoStrategyBase + uniform_strategy.UniformStrategyBase + +.. topic:: Samplers + + .. autosummary:: + :toctree: generated + + fifo_strategy.FifoSampleStrategy + uniform_strategy.UniformSampleStrategy + + +.. topic:: Samplers + + .. autosummary:: + :toctree: generated + + fifo_strategy.FifoEjectionStrategy + uniform_strategy.UniformEjectionStrategy + + +.. contents:: emote.memory + :depth: 3 + :local: + :backlinks: top diff --git a/emote/callback.py b/emote/callback.py index 205f3b9c..725ec788 100644 --- a/emote/callback.py +++ b/emote/callback.py @@ -182,9 +182,12 @@ def __init__(self, cycle=0): self.cycle = cycle def begin_training(self, *args, **kwargs): + '''Called when training starts, both from scratch and when restoring + from a checkpoint.''' pass def begin_cycle(self, *args, **kwargs): + '''Called at the start of each cycle.''' pass def begin_batch(self, *args, **kwargs): diff --git a/emote/memory/README.md b/emote/memory/README.md index efa20a30..e69de29b 100644 --- a/emote/memory/README.md +++ b/emote/memory/README.md @@ -1,48 +0,0 @@ -# High-level overview - -This directory contains all the major building blocks for our memory -implementation. The memory is borrowing from Reverb nomenclature, which in turn -is borrowing from databases. What is not alike Reverb is that we do not have the -RateSamplers (but it can be added). We also do not share data between ArrayTables. - -The goal of the memory is to provide a unified interface for all types of -machine learning tasks. This is achieved by focusing on configuration and -pluggability over code-driven functionality. - -Currently, there are three main points of customization: - -* Shape and type of data -* Insertion, sampling, and eviction -* Data transformation and generation - -## High-level parts - -### ArrayTable -A table is a datastructure containing a specific type of data that shares the same high-level structure. - -### Column -A column is a storage for a specific type of data where each item is the same shape and type - -### VirtualColumn -A virtual column is like a column, but it references another column and does -data synthesization or modification w.r.t that. For example, dones and masks are -synthetic data based only on indices. - -### Adaptors - -Adaptors are another approach to virtual column but are more suited for -transforming the whole batch, such as scaling for reshaping specific -datas. Since this step occurs when the data has already been converted to -tensors, the full power of Tensorflow is available here and gradients will be -correctly tracked. - -### Strategies, Samplers and Ejectors -Strategies are based on the delegate pattern, where we can inject implementation -details through objects instead of using inheritance. Strategies define the API -for sampling and ejection from memories, and are queried from the table upon -sampling and insertion. - -Samplers and Ejectors track the data (but do not own it!). They are used by the -table for sampling and ejection based on the policy they implement. Currently we -have Fifo and Uniform samplers and ejectors, but one could have prioritized -samplers/ejectors, etc. diff --git a/emote/memory/column.py b/emote/memory/column.py index 27890e32..c9648253 100644 --- a/emote/memory/column.py +++ b/emote/memory/column.py @@ -5,7 +5,7 @@ from dataclasses import dataclass from typing import Tuple, Type -from .storage import VirtualStorage +from emote.memory.storage import VirtualStorage @dataclass @@ -13,6 +13,8 @@ class Column: """A typed column for data storage.""" name: str + '''The name of the column''' + shape: Tuple[int] dtype: type diff --git a/emote/memory/memory.py b/emote/memory/memory.py index 83f99168..ed32951b 100644 --- a/emote/memory/memory.py +++ b/emote/memory/memory.py @@ -66,7 +66,7 @@ def __init__( self._term_states = [EpisodeState.TERMINAL, EpisodeState.INTERRUPTED] self._use_terminal = use_terminal - def is_initial(self, identity): + def is_initial(self, identity: int): """Returns true if identity is not already used in a partial sequence. Does not validate if the identity is associated with a complete episode.""" return identity not in self._store diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 00000000..f89255c8 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1677 @@ +[[package]] +name = "absl-py" +version = "1.0.0" +description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +six = "*" + +[[package]] +name = "alabaster" +version = "0.7.12" +description = "A configurable sidebar-enabled Sphinx theme" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "ale-py" +version = "0.7.4" +description = "The Arcade Learning Environment (ALE) - a platform for AI research." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = ">=4.10.0", markers = "python_version < \"3.10\""} +importlib-resources = "*" +numpy = "*" + +[package.extras] +test = ["pytest", "gym"] + +[[package]] +name = "argcomplete" +version = "2.0.0" +description = "Bash tab completion for argparse" +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.extras] +test = ["coverage", "flake8", "pexpect", "wheel"] + +[[package]] +name = "atomicwrites" +version = "1.4.0" +description = "Atomic file writes." +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + +[[package]] +name = "attrs" +version = "21.4.0" +description = "Classes Without Boilerplate" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" + +[package.extras] +dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] +docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] +tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"] + +[[package]] +name = "babel" +version = "2.9.1" +description = "Internationalization utilities" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + +[package.dependencies] +pytz = ">=2015.7" + +[[package]] +name = "black" +version = "22.3.0" +description = "The uncompromising code formatter." +category = "dev" +optional = false +python-versions = ">=3.6.2" + +[package.dependencies] +click = ">=8.0.0" +mypy-extensions = ">=0.4.3" +pathspec = ">=0.9.0" +platformdirs = ">=2" +tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} +typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""} + +[package.extras] +colorama = ["colorama (>=0.4.3)"] +d = ["aiohttp (>=3.7.4)"] +jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] +uvloop = ["uvloop (>=0.15.2)"] + +[[package]] +name = "boto" +version = "2.49.0" +description = "Amazon Web Services Library" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "cachetools" +version = "5.0.0" +description = "Extensible memoizing collections and decorators" +category = "main" +optional = false +python-versions = "~=3.7" + +[[package]] +name = "certifi" +version = "2021.10.8" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = "*" + +[[package]] +name = "cffi" +version = "1.15.0" +description = "Foreign Function Interface for Python calling C code." +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +pycparser = "*" + +[[package]] +name = "charset-normalizer" +version = "2.0.12" +description = "The Real First Universal Charset Detector. 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