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93 lines
4.1 KiB
Python
93 lines
4.1 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.architecture import Dense
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from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
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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 BoxActionSpace
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from rl_coach.spaces import SpacesDefinition
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class NAFHeadParameters(HeadParameters):
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def __init__(self, activation_function: str ='tanh', name: str='naf_head_params', dense_layer=Dense):
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super().__init__(parameterized_class=NAFHead, activation_function=activation_function, name=name,
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dense_layer=dense_layer)
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class NAFHead(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):
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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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if not isinstance(self.spaces.action, BoxActionSpace):
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raise ValueError("NAF works only for continuous action spaces (BoxActionSpace)")
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self.name = 'naf_q_values_head'
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self.num_actions = self.spaces.action.shape[0]
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self.output_scale = self.spaces.action.max_abs_range
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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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# NAF
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self.action = tf.placeholder(tf.float32, [None, self.num_actions], name="action")
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self.input = self.action
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# V Head
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self.V = self.dense_layer(1)(input_layer, name='V')
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# mu Head
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mu_unscaled = self.dense_layer(self.num_actions)(input_layer, activation=self.activation_function, name='mu_unscaled')
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self.mu = tf.multiply(mu_unscaled, self.output_scale, name='mu')
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# A Head
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# l_vector is a vector that includes a lower-triangular matrix values
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self.l_vector = self.dense_layer((self.num_actions * (self.num_actions + 1)) / 2)(input_layer, name='l_vector')
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# Convert l to a lower triangular matrix and exponentiate its diagonal
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i = 0
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columns = []
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for col in range(self.num_actions):
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start_row = col
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num_non_zero_elements = self.num_actions - start_row
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zeros_column_part = tf.zeros_like(self.l_vector[:, 0:start_row])
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diag_element = tf.expand_dims(tf.exp(self.l_vector[:, i]), 1)
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non_zeros_non_diag_column_part = self.l_vector[:, (i + 1):(i + num_non_zero_elements)]
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columns.append(tf.concat([zeros_column_part, diag_element, non_zeros_non_diag_column_part], axis=1))
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i += num_non_zero_elements
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self.L = tf.transpose(tf.stack(columns, axis=1), (0, 2, 1))
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# P = L*L^T
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self.P = tf.matmul(self.L, tf.transpose(self.L, (0, 2, 1)))
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# A = -1/2 * (u - mu)^T * P * (u - mu)
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action_diff = tf.expand_dims(self.action - self.mu, -1)
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a_matrix_form = -0.5 * tf.matmul(tf.transpose(action_diff, (0, 2, 1)), tf.matmul(self.P, action_diff))
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self.A = tf.reshape(a_matrix_form, [-1, 1])
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# Q Head
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self.Q = tf.add(self.V, self.A, name='Q')
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self.output = self.Q
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