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72 lines
2.2 KiB
Python
72 lines
2.2 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 rl_coach.base_parameters import AgentParameters
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from rl_coach.spaces import SpacesDefinition
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class Architecture(object):
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def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, name: str= ""):
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"""
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:param agent_parameters: the agent parameters
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:param spaces: the spaces (observation, action, etc.) definition of the agent
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:param name: the name of the network
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"""
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# spaces
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self.spaces = spaces
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self.name = name
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self.network_wrapper_name = self.name.split('/')[0] # the name can be main/online and the network_wrapper_name will be main
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self.full_name = "{}/{}".format(agent_parameters.full_name_id, name)
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self.network_parameters = agent_parameters.network_wrappers[self.network_wrapper_name]
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self.batch_size = self.network_parameters.batch_size
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self.learning_rate = self.network_parameters.learning_rate
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self.optimizer = None
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self.ap = agent_parameters
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def get_model(self):
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pass
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def predict(self, inputs):
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pass
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def train_on_batch(self, inputs, targets):
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pass
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def get_weights(self):
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pass
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def set_weights(self, weights, rate=1.0):
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pass
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def reset_accumulated_gradients(self):
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pass
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def accumulate_gradients(self, inputs, targets):
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pass
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def apply_and_reset_gradients(self, gradients):
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pass
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def apply_gradients(self, gradients):
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pass
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def get_variable_value(self, variable):
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pass
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def set_variable_value(self, assign_op, value, placeholder=None):
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pass
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