-
Notifications
You must be signed in to change notification settings - Fork 364
/
mbsac.py
406 lines (341 loc) · 15.4 KB
/
mbsac.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
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
from typing import Dict, Any, List
from functools import partial
import torch
from torch import Tensor
from torch import nn
from torch.distributions import Normal, Independent
from ding.torch_utils import to_device, fold_batch, unfold_batch, unsqueeze_repeat
from ding.utils import POLICY_REGISTRY
from ding.policy import SACPolicy
from ding.rl_utils import generalized_lambda_returns
from ding.policy.common_utils import default_preprocess_learn
from .utils import q_evaluation
@POLICY_REGISTRY.register('mbsac')
class MBSACPolicy(SACPolicy):
"""
Overview:
Model based SAC with value expansion (arXiv: 1803.00101)
and value gradient (arXiv: 1510.09142) w.r.t lambda-return.
https://arxiv.org/pdf/1803.00101.pdf
https://arxiv.org/pdf/1510.09142.pdf
Config:
== ==================== ======== ============= ==================================
ID Symbol Type Default Value Description
== ==================== ======== ============= ==================================
1 ``learn._lambda`` float 0.8 | Lambda for TD-lambda return.
2 ``learn.grad_clip` float 100.0 | Max norm of gradients.
3 | ``learn.sample`` bool True | Whether to sample states or
| ``_state`` | transitions from env buffer.
== ==================== ======== ============= ==================================
.. note::
For other configs, please refer to ding.policy.sac.SACPolicy.
"""
config = dict(
learn=dict(
# (float) Lambda for TD-lambda return.
lambda_=0.8,
# (float) Max norm of gradients.
grad_clip=100,
# (bool) Whether to sample states or transitions from environment buffer.
sample_state=True,
)
)
def _init_learn(self) -> None:
super()._init_learn()
self._target_model.requires_grad_(False)
self._lambda = self._cfg.learn.lambda_
self._grad_clip = self._cfg.learn.grad_clip
self._sample_state = self._cfg.learn.sample_state
self._auto_alpha = self._cfg.learn.auto_alpha
# TODO: auto alpha
assert not self._auto_alpha, "NotImplemented"
# TODO: TanhTransform leads to NaN
def actor_fn(obs: Tensor):
# (mu, sigma) = self._learn_model.forward(
# obs, mode='compute_actor')['logit']
# # enforce action bounds
# dist = TransformedDistribution(
# Independent(Normal(mu, sigma), 1), [TanhTransform()])
# action = dist.rsample()
# log_prob = dist.log_prob(action)
# return action, -self._alpha.detach() * log_prob
(mu, sigma) = self._learn_model.forward(obs, mode='compute_actor')['logit']
dist = Independent(Normal(mu, sigma), 1)
pred = dist.rsample()
action = torch.tanh(pred)
log_prob = dist.log_prob(
pred
) + 2 * (pred + torch.nn.functional.softplus(-2. * pred) - torch.log(torch.tensor(2.))).sum(-1)
return action, -self._alpha.detach() * log_prob
self._actor_fn = actor_fn
def critic_fn(obss: Tensor, actions: Tensor, model: nn.Module):
eval_data = {'obs': obss, 'action': actions}
q_values = model.forward(eval_data, mode='compute_critic')['q_value']
return q_values
self._critic_fn = critic_fn
self._forward_learn_cnt = 0
def _forward_learn(self, data: dict, world_model, envstep) -> Dict[str, Any]:
# preprocess data
data = default_preprocess_learn(
data,
use_priority=self._priority,
use_priority_IS_weight=self._cfg.priority_IS_weight,
ignore_done=self._cfg.learn.ignore_done,
use_nstep=False
)
if self._cuda:
data = to_device(data, self._device)
if len(data['action'].shape) == 1:
data['action'] = data['action'].unsqueeze(1)
self._learn_model.train()
self._target_model.train()
# TODO: use treetensor
# rollout length is determined by world_model.rollout_length_scheduler
if self._sample_state:
# data['reward'], ... are not used
obss, actions, rewards, aug_rewards, dones = \
world_model.rollout(data['obs'], self._actor_fn, envstep)
else:
obss, actions, rewards, aug_rewards, dones = \
world_model.rollout(data['next_obs'], self._actor_fn, envstep)
obss = torch.cat([data['obs'].unsqueeze(0), obss])
actions = torch.cat([data['action'].unsqueeze(0), actions])
rewards = torch.cat([data['reward'].unsqueeze(0), rewards])
aug_rewards = torch.cat([torch.zeros_like(data['reward']).unsqueeze(0), aug_rewards])
dones = torch.cat([data['done'].unsqueeze(0), dones])
dones = torch.cat([torch.zeros_like(data['done']).unsqueeze(0), dones])
# (T+1, B)
target_q_values = q_evaluation(obss, actions, partial(self._critic_fn, model=self._target_model))
if self._twin_critic:
target_q_values = torch.min(target_q_values[0], target_q_values[1]) + aug_rewards
else:
target_q_values = target_q_values + aug_rewards
# (T, B)
lambda_return = generalized_lambda_returns(target_q_values, rewards, self._gamma, self._lambda, dones[1:])
# (T, B)
# If S_t terminates, we should not consider loss from t+1,...
weight = (1 - dones[:-1].detach()).cumprod(dim=0)
# (T+1, B)
q_values = q_evaluation(obss.detach(), actions.detach(), partial(self._critic_fn, model=self._learn_model))
if self._twin_critic:
critic_loss = 0.5 * torch.square(q_values[0][:-1] - lambda_return.detach()) \
+ 0.5 * torch.square(q_values[1][:-1] - lambda_return.detach())
else:
critic_loss = 0.5 * torch.square(q_values[:-1] - lambda_return.detach())
# value expansion loss
critic_loss = (critic_loss * weight).mean()
# value gradient loss
policy_loss = -(lambda_return * weight).mean()
# alpha_loss = None
loss_dict = {
'critic_loss': critic_loss,
'policy_loss': policy_loss,
# 'alpha_loss': alpha_loss.detach(),
}
norm_dict = self._update(loss_dict)
# =============
# after update
# =============
self._forward_learn_cnt += 1
# target update
self._target_model.update(self._learn_model.state_dict())
return {
'cur_lr_q': self._optimizer_q.defaults['lr'],
'cur_lr_p': self._optimizer_policy.defaults['lr'],
'alpha': self._alpha.item(),
'target_q_value': target_q_values.detach().mean().item(),
**norm_dict,
**loss_dict,
}
def _update(self, loss_dict):
# update critic
self._optimizer_q.zero_grad()
loss_dict['critic_loss'].backward()
critic_norm = nn.utils.clip_grad_norm_(self._model.critic.parameters(), self._grad_clip)
self._optimizer_q.step()
# update policy
self._optimizer_policy.zero_grad()
loss_dict['policy_loss'].backward()
policy_norm = nn.utils.clip_grad_norm_(self._model.actor.parameters(), self._grad_clip)
self._optimizer_policy.step()
# update temperature
# self._alpha_optim.zero_grad()
# loss_dict['alpha_loss'].backward()
# self._alpha_optim.step()
return {'policy_norm': policy_norm, 'critic_norm': critic_norm}
def _monitor_vars_learn(self) -> List[str]:
r"""
Overview:
Return variables' name if variables are to used in monitor.
Returns:
- vars (:obj:`List[str]`): Variables' name list.
"""
alpha_loss = ['alpha_loss'] if self._auto_alpha else []
return [
'policy_loss',
'critic_loss',
'policy_norm',
'critic_norm',
'cur_lr_q',
'cur_lr_p',
'alpha',
'target_q_value',
] + alpha_loss
@POLICY_REGISTRY.register('stevesac')
class STEVESACPolicy(SACPolicy):
r"""
Overview:
Model based SAC with stochastic value expansion (arXiv 1807.01675).\
This implementation also uses value gradient w.r.t the same STEVE target.
https://arxiv.org/pdf/1807.01675.pdf
Config:
== ==================== ======== ============= =====================================
ID Symbol Type Default Value Description
== ==================== ======== ============= =====================================
1 ``learn.grad_clip` float 100.0 | Max norm of gradients.
2 ``learn.ensemble_size`` int 1 | The number of ensemble world models.
== ==================== ======== ============= =====================================
.. note::
For other configs, please refer to ding.policy.sac.SACPolicy.
"""
config = dict(
learn=dict(
# (float) Max norm of gradients.
grad_clip=100,
# (int) The number of ensemble world models.
ensemble_size=1,
)
)
def _init_learn(self) -> None:
super()._init_learn()
self._target_model.requires_grad_(False)
self._grad_clip = self._cfg.learn.grad_clip
self._ensemble_size = self._cfg.learn.ensemble_size
self._auto_alpha = self._cfg.learn.auto_alpha
# TODO: auto alpha
assert not self._auto_alpha, "NotImplemented"
def actor_fn(obs: Tensor):
obs, dim = fold_batch(obs, 1)
(mu, sigma) = self._learn_model.forward(obs, mode='compute_actor')['logit']
dist = Independent(Normal(mu, sigma), 1)
pred = dist.rsample()
action = torch.tanh(pred)
log_prob = dist.log_prob(
pred
) + 2 * (pred + torch.nn.functional.softplus(-2. * pred) - torch.log(torch.tensor(2.))).sum(-1)
aug_reward = -self._alpha.detach() * log_prob
return unfold_batch(action, dim), unfold_batch(aug_reward, dim)
self._actor_fn = actor_fn
def critic_fn(obss: Tensor, actions: Tensor, model: nn.Module):
eval_data = {'obs': obss, 'action': actions}
q_values = model.forward(eval_data, mode='compute_critic')['q_value']
return q_values
self._critic_fn = critic_fn
self._forward_learn_cnt = 0
def _forward_learn(self, data: dict, world_model, envstep) -> Dict[str, Any]:
# preprocess data
data = default_preprocess_learn(
data,
use_priority=self._priority,
use_priority_IS_weight=self._cfg.priority_IS_weight,
ignore_done=self._cfg.learn.ignore_done,
use_nstep=False
)
if self._cuda:
data = to_device(data, self._device)
if len(data['action'].shape) == 1:
data['action'] = data['action'].unsqueeze(1)
# [B, D] -> [E, B, D]
data['next_obs'] = unsqueeze_repeat(data['next_obs'], self._ensemble_size)
data['reward'] = unsqueeze_repeat(data['reward'], self._ensemble_size)
data['done'] = unsqueeze_repeat(data['done'], self._ensemble_size)
self._learn_model.train()
self._target_model.train()
obss, actions, rewards, aug_rewards, dones = \
world_model.rollout(data['next_obs'], self._actor_fn, envstep, keep_ensemble=True)
rewards = torch.cat([data['reward'].unsqueeze(0), rewards])
dones = torch.cat([data['done'].unsqueeze(0), dones])
# (T, E, B)
target_q_values = q_evaluation(obss, actions, partial(self._critic_fn, model=self._target_model))
if self._twin_critic:
target_q_values = torch.min(target_q_values[0], target_q_values[1]) + aug_rewards
else:
target_q_values = target_q_values + aug_rewards
# (T+1, E, B)
discounts = ((1 - dones) * self._gamma).cumprod(dim=0)
discounts = torch.cat([torch.ones_like(discounts)[:1], discounts])
# (T, E, B)
cum_rewards = (rewards * discounts[:-1]).cumsum(dim=0)
discounted_q_values = target_q_values * discounts[1:]
steve_return = cum_rewards + discounted_q_values
# (T, B)
steve_return_mean = steve_return.mean(1)
with torch.no_grad():
steve_return_inv_var = 1 / (1e-8 + steve_return.var(1, unbiased=False))
steve_return_weight = steve_return_inv_var / (1e-8 + steve_return_inv_var.sum(dim=0))
# (B, )
steve_return = (steve_return_mean * steve_return_weight).sum(0)
eval_data = {'obs': data['obs'], 'action': data['action']}
q_values = self._learn_model.forward(eval_data, mode='compute_critic')['q_value']
if self._twin_critic:
critic_loss = 0.5 * torch.square(q_values[0] - steve_return.detach()) \
+ 0.5 * torch.square(q_values[1] - steve_return.detach())
else:
critic_loss = 0.5 * torch.square(q_values - steve_return.detach())
critic_loss = critic_loss.mean()
policy_loss = -steve_return.mean()
# alpha_loss = None
loss_dict = {
'critic_loss': critic_loss,
'policy_loss': policy_loss,
# 'alpha_loss': alpha_loss.detach(),
}
norm_dict = self._update(loss_dict)
# =============
# after update
# =============
self._forward_learn_cnt += 1
# target update
self._target_model.update(self._learn_model.state_dict())
return {
'cur_lr_q': self._optimizer_q.defaults['lr'],
'cur_lr_p': self._optimizer_policy.defaults['lr'],
'alpha': self._alpha.item(),
'target_q_value': target_q_values.detach().mean().item(),
**norm_dict,
**loss_dict,
}
def _update(self, loss_dict):
# update critic
self._optimizer_q.zero_grad()
loss_dict['critic_loss'].backward()
critic_norm = nn.utils.clip_grad_norm_(self._model.critic.parameters(), self._grad_clip)
self._optimizer_q.step()
# update policy
self._optimizer_policy.zero_grad()
loss_dict['policy_loss'].backward()
policy_norm = nn.utils.clip_grad_norm_(self._model.actor.parameters(), self._grad_clip)
self._optimizer_policy.step()
# update temperature
# self._alpha_optim.zero_grad()
# loss_dict['alpha_loss'].backward()
# self._alpha_optim.step()
return {'policy_norm': policy_norm, 'critic_norm': critic_norm}
def _monitor_vars_learn(self) -> List[str]:
r"""
Overview:
Return variables' name if variables are to used in monitor.
Returns:
- vars (:obj:`List[str]`): Variables' name list.
"""
alpha_loss = ['alpha_loss'] if self._auto_alpha else []
return [
'policy_loss',
'critic_loss',
'policy_norm',
'critic_norm',
'cur_lr_q',
'cur_lr_p',
'alpha',
'target_q_value',
] + alpha_loss