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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
55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
#
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# Copyright (c) 2017 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, normalized_columns_initializer
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from rl_coach.base_parameters import AgentParameters
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from rl_coach.core_types import VStateValue
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from rl_coach.spaces import SpacesDefinition
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class VHead(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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dense_layer=Dense, initializer='normalized_columns'):
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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 = 'v_values_head'
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self.return_type = VStateValue
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if agent_parameters.network_wrappers[self.network_name.split('/')[0]].replace_mse_with_huber_loss:
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self.loss_type = tf.losses.huber_loss
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else:
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self.loss_type = tf.losses.mean_squared_error
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self.initializer = initializer
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def _build_module(self, input_layer):
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# Standard V Network
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if self.initializer == 'normalized_columns':
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self.output = self.dense_layer(1)(input_layer, name='output',
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kernel_initializer=normalized_columns_initializer(1.0))
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elif self.initializer == 'xavier' or self.initializer is None:
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self.output = self.dense_layer(1)(input_layer, name='output')
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def __str__(self):
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result = [
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"Dense (num outputs = 1)"
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]
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return '\n'.join(result)
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