-
Notifications
You must be signed in to change notification settings - Fork 4
/
main.py
285 lines (228 loc) · 11.5 KB
/
main.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
import numpy as np
import torch
import json
from maml_rl.metalearner import MetaLearner, HierarchicalMetaLearner
from maml_rl.policies import CategoricalMLPPolicy, NormalMLPPolicy
from maml_rl.baseline import LinearFeatureBaseline
from maml_rl.sampler import BatchSampler
from maml_rl.policies.empowerment_skills import EmpowermentSkills
from rlkit.rlkit.torch.sac.policies import TanhGaussianPolicy
from rlkit.rlkit.torch.networks import FlattenMlp
from maml_rl.envs.mujoco.pusher import PusherEnv
from tensorboardX import SummaryWriter
import warnings
warnings.filterwarnings("ignore")
torch.manual_seed(7)
seed = 7
def total_rewards(episodes_rewards, aggregation=torch.mean):
rewards = torch.mean(torch.stack([aggregation(torch.sum(rewards, dim=0))
for rewards in episodes_rewards], dim=0))
return rewards.item()
def hierarchical_meta_policy(env, skills_dim, sampler, output_size, net_size):
higher_policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
skills_dim,
hidden_sizes=(args.hidden_size,) * args.num_layers)
observation_dim = int(np.prod(sampler.envs.observation_space.shape))
action_dim = int(np.prod(sampler.envs.action_space.shape))
hidden_size = net_size
output_size = output_size
skills_dim = skills_dim
# Define the networks
q_value_function_1 = FlattenMlp(
hidden_sizes=[hidden_size, hidden_size],
input_size=observation_dim + action_dim + skills_dim,
output_size=output_size)
q_value_function_2 = FlattenMlp(
hidden_sizes=[hidden_size, hidden_size],
input_size=observation_dim + action_dim + skills_dim,
output_size=output_size)
value_function = FlattenMlp(
hidden_sizes=[hidden_size, hidden_size],
input_size=observation_dim,
output_size=output_size
)
discriminator_function = FlattenMlp(
hidden_sizes=[hidden_size, hidden_size],
input_size=observation_dim,
output_size=skills_dim
)
policy = TanhGaussianPolicy(
hidden_sizes=[hidden_size, hidden_size],
obs_dim=observation_dim + skills_dim,
action_dim=action_dim
)
# Define the empowerment skills algorithm
env_pusher = PusherEnv()
algorithm = EmpowermentSkills(env=env_pusher,
policy=policy,
higher_policy=higher_policy,
discriminator=discriminator_function,
q_value_function_1=q_value_function_1,
q_value_function_2=q_value_function_2,
value_function=value_function)
lower_policy = algorithm
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape))
)
return higher_policy, lower_policy, baseline
def main(args):
continuous_actions = (args.env_name in ['AntVel-v1', 'AntDir-v1',
'AntPos-v0', 'HalfCheetahVel-v1', 'HalfCheetahDir-v1',
'2DNavigation-v0', 'Pusher'])
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device.type)
json.dump(config, f, indent=2)
if not args.hierarchical:
sampler = BatchSampler(args.env_name, batch_size=args.fast_batch_size,
num_workers=args.num_workers)
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size,) * args.num_layers)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size,) * args.num_layers)
baseline = LinearFeatureBaseline(
int(np.prod(sampler.envs.observation_space.shape)))
metalearner = MetaLearner(sampler, policy, baseline, gamma=args.gamma,
fast_lr=args.fast_lr, tau=args.tau, device=args.device)
for i, batch in enumerate(range(args.num_batches)):
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks, first_order=args.first_order)
metalearner.step(episodes, max_kl=args.max_kl, cg_iters=args.cg_iters,
cg_damping=args.cg_damping, ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
print('Total Rewards', str(total_rewards([ep.rewards for _, ep in episodes])))
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]), batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]), batch)
if (i+1) % args.save_every== 0:
# Save policy network
with open(os.path.join(save_folder,
'policy-{0}.pt'.format(batch)), 'wb') as f:
torch.save(policy, f)
else:
sampler = BatchSampler(args.env_name, batch_size=args.fast_batch_size,
num_workers=args.num_workers)
# Get the policies
higher_policy, lower_trainer, baseline = hierarchical_meta_policy(args.env_name,
args.skills_dim,
sampler=sampler,
net_size=args.hidden_size,
output_size=1)
# Define the hierarchical meta learner
hr_meta_learner = HierarchicalMetaLearner(sampler, higher_policy,
baseline, gamma=args.gamma,
fast_lr=args.fast_lr, tau=args.tau, device=args.device)
# Training procedure
for i, batch in enumerate(range(args.num_batches)):
# Train the lower level policy
lower_trainer.train()
# Now freeze the lower level policy
lower_networks = lower_trainer.networks
lower_policy = lower_networks[0]
lower_policy.trainable = False
# Sample the different tasks
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
# Sample the different episodes for the different tasks
episodes = hr_meta_learner.sample(tasks, lower_policy, first_order=args.first_order)
hr_meta_learner.step(episodes, max_kl=args.max_kl, cg_iters=args.cg_iters,
cg_damping=args.cg_damping, ls_max_steps=args.ls_max_steps,
ls_backtrack_ratio=args.ls_backtrack_ratio)
print('Total Rewards', str(total_rewards([ep.rewards for _, ep in episodes])))
lower_policy.trainable = True
# Tensorboard
writer.add_scalar('total_rewards/before_update',
total_rewards([ep.rewards for ep, _ in episodes]), batch)
writer.add_scalar('total_rewards/after_update',
total_rewards([ep.rewards for _, ep in episodes]), batch)
if (i+1) % args.save_every == 0:
# Save the policy networks
with open(os.path.join(save_folder,
'h_policy-{0}.pt'.format(batch)), 'wb') as f:
torch.save(higher_policy, f)
with open(os.path.join(save_folder,
'l_policy-{0}.pt'.format(batch)), 'wb') as f:
torch.save(lower_policy, f)
with open(os.path.join(save_folder, 'baseline.pt'), 'wb') as f:
torch.save(baseline, f)
if __name__ == '__main__':
import argparse
import os
import multiprocessing as mp
parser = argparse.ArgumentParser(description='Reinforcement learning with '
'Model-Agnostic Meta-Learning (MAML)')
# General
parser.add_argument('--env-name', type=str, default='Pusher',
help='name of the environment')
parser.add_argument('--gamma', type=float, default=0.99,
help='value of the discount factor gamma')
parser.add_argument('--tau', type=float, default=1.0,
help='value of the discount factor for GAE')
parser.add_argument('--first-order', action='store_true',
help='use the first-order approximation of MAML')
parser.add_argument('--hierarchical', type=bool, default=True,
help='whether to use a hierarchical policy or not')
parser.add_argument('--skills-dim', type=int, default=50,
help='The dimension of skills to be learned by the empowerment model')
# Policy network (relu activation function)
parser.add_argument('--hidden-size', type=int, default=100,
help='number of hidden units per layer')
parser.add_argument('--num-layers', type=int, default=2,
help='number of hidden layers')
# Task-specific
parser.add_argument('--fast-batch-size', type=int, default=60,
help='batch size for each individual task')
parser.add_argument('--fast-lr', type=float, default=0.5,
help='learning rate for the 1-step gradient update of MAML')
parser.add_argument('--max-path-length', type=int, default=100,
help='Maximum length of a single rollout')
# Optimization
parser.add_argument('--num-batches', type=int, default=1000,
help='number of batches')
parser.add_argument('--meta-batch-size', type=int, default=20,
help='number of tasks per batch')
parser.add_argument('--max-kl', type=float, default=1e-2,
help='maximum value for the KL constraint in TRPO')
parser.add_argument('--cg-iters', type=int, default=10,
help='number of iterations of conjugate gradient')
parser.add_argument('--cg-damping', type=float, default=1e-5,
help='damping in conjugate gradient')
parser.add_argument('--ls-max-steps', type=int, default=15,
help='maximum number of iterations for line search')
parser.add_argument('--ls-backtrack-ratio', type=float, default=0.8,
help='maximum number of iterations for line search')
# Miscellaneous
parser.add_argument('--output-folder', type=str, default='maml-pusher',
help='name of the output folder')
parser.add_argument('--num-workers', type=int, default=mp.cpu_count() - 1,
help='number of workers for trajectories sampling')
parser.add_argument('--device', type=str, default='cpu',
help='set the device (cpu or cuda)')
parser.add_argument('--save-every', type=int, default=50,
help='save the policy every n epochs')
args = parser.parse_args()
print("Using ", str(mp.cpu_count()-1), " number of workers")
# Create logs and saves folder if they don't exist
if not os.path.exists('./logs'):
os.makedirs('./logs')
if not os.path.exists('./saves'):
os.makedirs('./saves')
# Device
args.device = torch.device(args.device
if torch.cuda.is_available() else 'cpu')
# Slurm
if 'SLURM_JOB_ID' in os.environ:
args.output_folder += '-{0}'.format(os.environ['SLURM_JOB_ID'])
main(args)