google-research

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# coding=utf-8
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# Copyright 2024 The Google Research Authors.
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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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# pylint: disable=g-complex-comprehension
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# pylint: disable=missing-docstring
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"""MLP+LSTM network for use with MuZero."""
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import tensorflow as tf
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from muzero import network
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class MLPandLSTM(network.AbstractEncoderandLSTM):
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  def __init__(self, mlp_sizes, *args, **kwargs):
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    super().__init__(*args, **kwargs)
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    mlp_layers = [
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        tf.keras.Sequential([
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            tf.keras.layers.Dense(size, activation='relu', use_bias=False),
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            tf.keras.layers.LayerNormalization(),
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        ],
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                            name='intermediate_{}'.format(idx))
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        for idx, size in enumerate(mlp_sizes)
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    ]
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    self._observation_encoder = tf.keras.Sequential(
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        mlp_layers, name='observation_encoder')
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  def _encode_observation(self, observation, training=True):
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    return self._observation_encoder(observation, training=training)
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