-
Notifications
You must be signed in to change notification settings - Fork 29
/
actor_critic.py
89 lines (79 loc) · 2.9 KB
/
actor_critic.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
# Copyright (C) 2018 Heron Systems, Inc.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from adept.actor import ACRolloutActorTrain
from adept.exp import Rollout
from adept.learner import ACRolloutLearner
from .base.agent_module import AgentModule
class ActorCritic(AgentModule):
args = {**Rollout.args, **ACRolloutActorTrain.args, **ACRolloutLearner.args}
def __init__(
self,
reward_normalizer,
action_space,
spec_builder,
rollout_len,
discount,
normalize_advantage,
entropy_weight,
return_scale,
):
super(ActorCritic, self).__init__(reward_normalizer, action_space)
self.discount = discount
self.normalize_advantage = normalize_advantage
self.entropy_weight = entropy_weight
self._exp_cache = Rollout(spec_builder, rollout_len)
self._actor = ACRolloutActorTrain(action_space)
self._learner = ACRolloutLearner(
reward_normalizer,
discount,
normalize_advantage,
entropy_weight,
return_scale,
)
@classmethod
def from_args(
cls, args, reward_normalizer, action_space, spec_builder, **kwargs
):
return cls(
reward_normalizer,
action_space,
spec_builder,
rollout_len=args.rollout_len,
discount=args.discount,
normalize_advantage=args.normalize_advantage,
entropy_weight=args.entropy_weight,
return_scale=args.return_scale,
)
@property
def exp_cache(self):
return self._exp_cache
@classmethod
def _exp_spec(cls, exp_len, batch_sz, obs_space, act_space, internal_space):
return ACRolloutActorTrain._exp_spec(
exp_len, batch_sz, obs_space, act_space, internal_space
)
@staticmethod
def output_space(action_space):
return ACRolloutActorTrain.output_space(action_space)
def compute_action_exp(
self, predictions, internals, obs, available_actions
):
return self._actor.compute_action_exp(
predictions, internals, obs, available_actions
)
def learn_step(self, updater, network, next_obs, internals):
return self._learner.learn_step(
updater, network, self.exp_cache.read(), next_obs, internals
)