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