mirror of
https://github.com/gryf/coach.git
synced 2025-12-18 19:50:17 +01:00
74db141d5e
* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
108 lines
5.0 KiB
Python
108 lines
5.0 KiB
Python
#
|
|
# Copyright (c) 2019 Intel Corporation
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import tensorflow as tf
|
|
|
|
from rl_coach.architectures.tensorflow_components.layers import Dense
|
|
from rl_coach.architectures.tensorflow_components.heads.head import Head
|
|
from rl_coach.base_parameters import AgentParameters
|
|
from rl_coach.core_types import ActionProbabilities
|
|
from rl_coach.spaces import SpacesDefinition
|
|
from rl_coach.utils import eps
|
|
|
|
LOG_SIG_CAP_MAX = 2
|
|
LOG_SIG_CAP_MIN = -20
|
|
|
|
|
|
class SACPolicyHead(Head):
|
|
def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
|
|
head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',
|
|
squash: bool = True, dense_layer=Dense):
|
|
super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function,
|
|
dense_layer=dense_layer)
|
|
self.name = 'sac_policy_head'
|
|
self.return_type = ActionProbabilities
|
|
self.num_actions = self.spaces.action.shape # continuous actions
|
|
self.squash = squash # squashing using tanh
|
|
|
|
def _build_module(self, input_layer):
|
|
self.given_raw_actions = tf.placeholder(tf.float32, [None, self.num_actions], name="actions")
|
|
self.input = [self.given_raw_actions]
|
|
self.output = []
|
|
|
|
# build the network
|
|
self._build_continuous_net(input_layer, self.spaces.action)
|
|
|
|
def _squash_correction(self,actions):
|
|
'''
|
|
correct squash operation (in case of bounded actions) according to appendix C in the paper.
|
|
NOTE : this correction assume the squash is done with tanh.
|
|
:param actions: unbounded actions
|
|
:return: the correction to be applied to the log_prob of the actions, assuming tanh squash
|
|
'''
|
|
if not self.squash:
|
|
return 0
|
|
return tf.reduce_sum(tf.log(1 - tf.tanh(actions) ** 2 + eps), axis=1)
|
|
|
|
def _build_continuous_net(self, input_layer, action_space):
|
|
num_actions = action_space.shape[0]
|
|
|
|
self.policy_mu_and_logsig = self.dense_layer(2*num_actions)(input_layer, name='policy_mu_logsig')
|
|
self.policy_mean = tf.identity(self.policy_mu_and_logsig[..., :num_actions], name='policy_mean')
|
|
self.policy_log_std = tf.clip_by_value(self.policy_mu_and_logsig[..., num_actions:],
|
|
LOG_SIG_CAP_MIN, LOG_SIG_CAP_MAX,name='policy_log_std')
|
|
|
|
self.output.append(self.policy_mean) # output[0]
|
|
self.output.append(self.policy_log_std) # output[1]
|
|
|
|
# define the distributions for the policy
|
|
# Tensorflow's multivariate normal distribution supports reparameterization
|
|
tfd = tf.contrib.distributions
|
|
self.policy_distribution = tfd.MultivariateNormalDiag(loc=self.policy_mean,
|
|
scale_diag=tf.exp(self.policy_log_std))
|
|
|
|
# define network outputs
|
|
# note that tensorflow supports reparametrization.
|
|
# i.e. policy_action_sample is a tensor through which gradients can flow
|
|
self.raw_actions = self.policy_distribution.sample()
|
|
|
|
if self.squash:
|
|
self.actions = tf.tanh(self.raw_actions)
|
|
# correct log_prob in case of squash (see appendix C in the paper)
|
|
squash_correction = self._squash_correction(self.raw_actions)
|
|
else:
|
|
self.actions = self.raw_actions
|
|
squash_correction = 0
|
|
|
|
# policy_action_logprob is a tensor through which gradients can flow
|
|
self.sampled_actions_logprob = self.policy_distribution.log_prob(self.raw_actions) - squash_correction
|
|
self.sampled_actions_logprob_mean = tf.reduce_mean(self.sampled_actions_logprob)
|
|
|
|
self.output.append(self.raw_actions) # output[2] : sampled raw action (before squash)
|
|
self.output.append(self.actions) # output[3] : squashed (if needed) version of sampled raw_actions
|
|
self.output.append(self.sampled_actions_logprob) # output[4]: log prob of sampled action (squash corrected)
|
|
self.output.append(self.sampled_actions_logprob_mean) # output[5]: mean of log prob of sampled actions (squash corrected)
|
|
|
|
def __str__(self):
|
|
result = [
|
|
"policy head:"
|
|
"\t\tDense (num outputs = 256)",
|
|
"\t\tDense (num outputs = 256)",
|
|
"\t\tDense (num outputs = {0})".format(2*self.num_actions),
|
|
"policy_mu = output[:num_actions], policy_std = output[num_actions:]"
|
|
]
|
|
return '\n'.join(result)
|