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70 lines
3.0 KiB
Python
70 lines
3.0 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
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from rl_coach.base_parameters import AgentParameters
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from rl_coach.core_types import QActionStateValue
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from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace
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class QHead(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, output_bias_initializer=None):
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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 = 'q_values_head'
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if isinstance(self.spaces.action, BoxActionSpace):
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self.num_actions = 1
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elif isinstance(self.spaces.action, DiscreteActionSpace):
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self.num_actions = len(self.spaces.action.actions)
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else:
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raise ValueError(
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'QHead does not support action spaces of type: {class_name}'.format(
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class_name=self.spaces.action.__class__.__name__,
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)
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)
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self.return_type = QActionStateValue
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if agent_parameters.network_wrappers[self.network_name].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.output_bias_initializer = output_bias_initializer
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def _build_module(self, input_layer):
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# Standard Q Network
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self.q_values = self.output = self.dense_layer(self.num_actions)\
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(input_layer, name='output', bias_initializer=self.output_bias_initializer)
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# used in batch-rl to estimate a probablity distribution over actions
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self.softmax = self.add_softmax_with_temperature()
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def __str__(self):
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result = [
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"Dense (num outputs = {})".format(self.num_actions)
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]
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return '\n'.join(result)
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def add_softmax_with_temperature(self):
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temperature = self.ap.network_wrappers[self.network_name].softmax_temperature
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temperature_scaled_outputs = self.q_values / temperature
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return tf.nn.softmax(temperature_scaled_outputs, name="softmax")
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