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67a90ee87e
* Allow arbitrary dimensional observation (non vector or image) * Added creating PlanarMapsObservationSpace to GymEnvironment when number of channels is not 1 or 3
42 lines
1.7 KiB
Python
42 lines
1.7 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 List, Union
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from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters
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class InputEmbedderParameters(NetworkComponentParameters):
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def __init__(self, activation_function: str='relu', scheme: Union[List, EmbedderScheme]=EmbedderScheme.Medium,
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batchnorm: bool=False, dropout_rate: float=0.0, name: str='embedder', input_rescaling=None,
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input_offset=None, input_clipping=None, dense_layer=None, is_training=False):
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super().__init__(dense_layer=dense_layer)
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self.activation_function = activation_function
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self.scheme = scheme
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self.batchnorm = batchnorm
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self.dropout_rate = dropout_rate
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if input_rescaling is None:
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input_rescaling = {'image': 255.0, 'vector': 1.0, 'tensor': 1.0}
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if input_offset is None:
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input_offset = {'image': 0.0, 'vector': 0.0, 'tensor': 0.0}
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self.input_rescaling = input_rescaling
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self.input_offset = input_offset
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self.input_clipping = input_clipping
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self.name = name
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self.is_training = is_training
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