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62 lines
2.6 KiB
Python
62 lines
2.6 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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from rl_coach.utils import force_list
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class RegressionHead(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, scheme=[Dense(256), Dense(256)]):
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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 = 'regression_head'
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self.scheme = scheme
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self.layers = []
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if isinstance(self.spaces.action, BoxActionSpace):
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self.num_actions = self.spaces.action.shape[0]
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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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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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def _build_module(self, input_layer):
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self.layers.append(input_layer)
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for idx, layer_params in enumerate(self.scheme):
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self.layers.extend(force_list(
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layer_params(input_layer=self.layers[-1], name='{}_{}'.format(layer_params.__class__.__name__, idx))
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))
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self.layers.append(self.dense_layer(self.num_actions)(self.layers[-1], name='output'))
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self.output = self.layers[-1]
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def __str__(self):
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result = []
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for layer in self.layers:
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result.append(str(layer))
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return '\n'.join(result)
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