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89 lines
3.3 KiB
Python
89 lines
3.3 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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from typing import Union, List
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import tensorflow as tf
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from rl_coach.architectures.tensorflow_components.architecture import batchnorm_activation_dropout, Dense
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from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
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from rl_coach.base_parameters import MiddlewareScheme
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from rl_coach.core_types import Middleware_FC_Embedding
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class FCMiddlewareParameters(MiddlewareParameters):
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def __init__(self, activation_function='relu',
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scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium,
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batchnorm: bool = False, dropout: bool = False,
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name="middleware_fc_embedder", dense_layer=Dense):
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super().__init__(parameterized_class=FCMiddleware, activation_function=activation_function,
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scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer)
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class FCMiddleware(Middleware):
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def __init__(self, activation_function=tf.nn.relu,
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scheme: MiddlewareScheme = MiddlewareScheme.Medium,
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batchnorm: bool = False, dropout: bool = False,
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name="middleware_fc_embedder", dense_layer=Dense):
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super().__init__(activation_function=activation_function, batchnorm=batchnorm,
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dropout=dropout, scheme=scheme, name=name, dense_layer=dense_layer)
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self.return_type = Middleware_FC_Embedding
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self.layers = []
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def _build_module(self):
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self.layers.append(self.input)
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if isinstance(self.scheme, MiddlewareScheme):
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layers_params = self.schemes[self.scheme]
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else:
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layers_params = self.scheme
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for idx, layer_params in enumerate(layers_params):
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self.layers.append(
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layer_params(self.layers[-1], name='{}_{}'.format(layer_params.__class__.__name__, idx))
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)
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self.layers.extend(batchnorm_activation_dropout(self.layers[-1], self.batchnorm,
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self.activation_function, self.dropout,
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self.dropout_rate, idx))
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self.output = self.layers[-1]
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@property
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def schemes(self):
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return {
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MiddlewareScheme.Empty:
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[],
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# ppo
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MiddlewareScheme.Shallow:
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[
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self.dense_layer([64])
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],
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# dqn
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MiddlewareScheme.Medium:
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[
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self.dense_layer([512])
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],
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MiddlewareScheme.Deep: \
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[
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self.dense_layer([128]),
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self.dense_layer([128]),
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self.dense_layer([128])
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]
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}
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