-
Notifications
You must be signed in to change notification settings - Fork 6
/
eval.py
372 lines (325 loc) · 12.5 KB
/
eval.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
import argparse
import time
import random
import numpy as np
import torch
from torch.autograd import Variable
from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete
from marl_algorithms.maddpg.maddpg import MADDPG
from marl_algorithms.iql.iql import IQL
from utilities.logger import Logger
from utilities.plotter import Plotter
from utilities.frame_saver import FrameSaver
class Eval:
def __init__(self):
self.parser = argparse.ArgumentParser(
"Reinforcement Learning experiments for multiagent environments"
)
self.parse_args()
self.arglist = self.parser.parse_args()
def parse_default_args(self):
"""
Parse default arguments for MARL training script
"""
self.parser.add_argument(
"--save_gifs", action="store_true", help="Save gif of episode into gifs directory"
)
# algorithm
self.parser.add_argument(
"--alg", type=str, default="maddpg", help="name of the algorithm to use"
)
self.parser.add_argument("--hidden_dim", default=128, type=int)
# curiosity
self.parser.add_argument(
"--curiosity", type=str, default=None, help="name of curiosity to use"
)
self.parser.add_argument(
"--joint_curiosity",
action="store_true",
default=False,
help="flag if curiosity should be applied jointly for all agents",
)
self.parser.add_argument(
"--curiosity_hidden_dim",
type=int,
default=64,
help="curiosity internal state representation size",
)
self.parser.add_argument(
"--curiosity_state_rep_size",
type=int,
default=64,
help="curiosity internal state representation size",
)
self.parser.add_argument(
"--count_key_dim",
type=int,
default=128,
help="key dimensionality of hash-count-based curiosity",
)
self.parser.add_argument(
"--count_decay", type=float, default=0.01, help="factor for count decay speed"
)
self.parser.add_argument(
"--eta", type=int, default=2, help="curiosity loss weighting factor"
)
self.parser.add_argument(
"--curiosity_lr",
type=float,
default=1e-5,
help="learning rate for curiosity optimizer",
)
self.parser.add_argument(
"--batch_size",
type=int,
default=1024,
help="number of episodes to optimize at the same time",
)
# training length
self.parser.add_argument(
"--num_episodes", type=int, default=100, help="number of episodes"
)
self.parser.add_argument(
"--max_episode_len", type=int, default=25, help="maximum episode length"
)
# core training parameters
self.parser.add_argument(
"--n_training_threads", default=6, type=int, help="number of training threads"
)
self.parser.add_argument("--gamma", type=float, default=0.9, help="discount factor")
self.parser.add_argument(
"--tau", type=float, default=0.01, help="tau as stepsize for target network updates"
)
self.parser.add_argument(
"--lr", type=float, default=0.01, help="learning rate for Adam optimizer"
)
self.parser.add_argument(
"--dropout_p", type=float, default=0.0, help="Dropout probability"
)
self.parser.add_argument(
"--seed", type=int, default=None, help="random seed used throughout training"
)
# exploration settings
self.parser.add_argument(
"--decay_factor", type=float, default=0.0, help="exploration decay factor"
)
self.parser.add_argument(
"--exploration_bonus", type=float, default=0.0, help="eploration bonus value"
)
self.parser.add_argument("--n_exploration_eps", default=1, type=int)
self.parser.add_argument("--init_noise_scale", default=0.0, type=float)
self.parser.add_argument("--final_noise_scale", default=0.0, type=float)
self.parser.add_argument(
"--run", type=str, default="default", help="run name for stored paths"
)
self.parser.add_argument(
"--load_models",
type=str,
default=None,
help="path where models should be loaded from if set",
)
self.parser.add_argument(
"--load_models_extension",
type=str,
default="final",
help="name extension for models to load",
)
def parse_args(self):
"""
parse own arguments
"""
self.parse_default_args()
def extract_sizes(self, spaces):
"""
Extract space dimensions
:param spaces: list of Gym spaces
:return: list of ints with sizes for each agent
"""
sizes = []
for space in spaces:
if isinstance(space, Box):
size = sum(space.shape)
elif isinstance(space, Dict):
size = sum(self.extract_sizes(space.values()))
elif isinstance(space, Discrete) or isinstance(space, MultiBinary):
size = space.n
elif isinstance(space, MultiDiscrete):
size = sum(space.nvec)
else:
raise ValueError("Unknown class of space: ", type(space))
sizes.append(size)
return sizes
def create_environment(self):
"""
Create environment instance
:return: environment (gym interface), env_name, task_name, n_agents, observation_sizes,
action_sizes, discrete_actions
"""
raise NotImplementedError()
def reset_environment(self):
"""
Reset environment for new episode
:return: observation (as torch tensor)
"""
raise NotImplementedError
def select_actions(self, obs):
"""
Select actions for agents
:param obs: joint observation
:return: action_tensor, action_list
"""
raise NotImplementedError()
def environment_step(self, actions):
"""
Take step in the environment
:param actions: actions to apply for each agent
:return: reward, done, next_obs (as Pytorch tensors)
"""
raise NotImplementedError()
def environment_render(self):
"""
Render visualisation of environment
"""
raise NotImplementedError()
def eval(self):
"""
Abstract evaluation flow
"""
print("EVALUATION RUN")
print("No exploration and dropout will be used")
self.arglist.exploration_bonus = 0.0
self.arglist.init_noise_scale = 0.0
self.arglist.dropout_p = 0.0
if self.arglist.load_models is None:
print("WARNING: Evaluation run without loading any models!")
# set random seeds before model creation
if self.arglist.seed is not None:
random.seed(self.arglist.seed)
np.random.seed(self.arglist.seed)
torch.manual_seed(self.arglist.seed)
torch.cuda.manual_seed(self.arglist.seed)
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
env, env_name, task_name, n_agents, observation_sizes, action_sizes, discrete_actions = (
self.create_environment()
)
self.env = env
self.n_agents = n_agents
print("Observation sizes: ", observation_sizes)
print("Action sizes: ", action_sizes)
# Create curiosity instances
if self.arglist.curiosity is None:
print("No curiosity is to be used!")
elif self.arglist.curiosity == "icm":
print("Training uses Intrinsic Curiosity Module (ICM)!")
elif self.arglist.curiosity == "rnd":
print("Training uses Random Network Distillation (RND)!")
elif self.arglist.curiosity == "count":
print("Training uses hash-based counting exploration bonus!")
# TODO: add count based ones
else:
raise ValueError("Unknown curiosity: " + self.arglist.curiosity)
# create algorithm trainer
if self.arglist.alg == "maddpg":
self.alg = MADDPG(
n_agents, observation_sizes, action_sizes, discrete_actions, self.arglist
)
print(
"Training multi-agent deep deterministic policy gradient (MADDPG) on "
+ env_name
+ " environment"
)
elif self.arglist.alg == "iql":
self.alg = IQL(
n_agents, observation_sizes, action_sizes, discrete_actions, self.arglist
)
print("Training independent q-learning (IQL) on " + env_name + " environment")
else:
raise ValueError("Unknown algorithm: " + self.arglist.alg)
# set random seeds past model creation
if self.arglist.seed is not None:
random.seed(self.arglist.seed)
np.random.seed(self.arglist.seed)
torch.manual_seed(self.arglist.seed)
torch.cuda.manual_seed(self.arglist.seed)
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if self.arglist.load_models is not None:
print(
"Loading models from "
+ self.arglist.load_models
+ " with extension "
+ self.arglist.load_models_extension
)
self.alg.load_model_networks(
self.arglist.load_models, "_" + self.arglist.load_models_extension
)
self.logger = Logger(n_agents, task_name, None, self.arglist.alg, self.arglist.curiosity)
self.plotter = Plotter(
self.logger,
n_agents,
task_name,
self.arglist.run,
self.arglist.alg,
self.arglist.curiosity,
)
if self.arglist.save_gifs:
self.frame_saver = FrameSaver(
self.arglist.eta, task_name, self.arglist.run, self.arglist.alg
)
print("Starting iterations...")
start_time = time.time()
t = 0
for ep in range(self.arglist.num_episodes):
episode_rewards = []
obs = self.reset_environment()
self.alg.reset(ep)
episode_rewards = np.array([0.0] * n_agents)
episode_length = 0
done = False
while not done and episode_length < self.arglist.max_episode_len:
torch_obs = [
Variable(torch.Tensor(obs[i]), requires_grad=False) for i in range(n_agents)
]
actions, agent_actions = self.select_actions(torch_obs)
rewards, dones, next_obs = self.environment_step(actions)
t += 1
episode_rewards += rewards
# for displaying learned policies
self.environment_render()
if self.arglist.save_gifs:
self.frame_saver.add_frame(self.env.render("rgb_array")[0], ep)
obs = next_obs
episode_length += 1
done = all(dones)
if self.arglist.alg == "maddpg":
self.logger.log_episode(
ep,
episode_rewards,
[0.0] * n_agents,
self.alg.agents[0].get_exploration_scale(),
)
if self.arglist.alg == "iql":
self.logger.log_episode(
ep,
episode_rewards,
[0.0] * n_agents,
self.alg.agents[0].epsilon,
)
self.logger.dump_episodes(1)
episode_rewards = []
episode_length = 0
if self.arglist.save_gifs:
self.frame_saver.save_episode_gif()
if ep % 20 == 0 and ep > 0:
# update plots
self.plotter.update_reward_plot(True)
self.plotter.update_exploration_plot(True)
duration = time.time() - start_time
print("Overall duration: %.2fs" % duration)
env.close()
if __name__ == "__main__":
ev = Eval()
ev.eval()