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82 lines
3.4 KiB
Python
82 lines
3.4 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
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import numpy as np
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from rl_coach.agents.imitation_agent import ImitationAgent
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from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
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from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
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from rl_coach.base_parameters import AgentParameters, AlgorithmParameters, NetworkParameters, InputEmbedderParameters, \
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MiddlewareScheme
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from rl_coach.exploration_policies.e_greedy import EGreedyParameters
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
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class BCAlgorithmParameters(AlgorithmParameters):
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def __init__(self):
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super().__init__()
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self.collect_new_data = False
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class BCNetworkParameters(NetworkParameters):
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def __init__(self):
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super().__init__()
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self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
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self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
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self.heads_parameters = [PolicyHeadParameters()]
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self.loss_weights = [1.0]
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self.optimizer_type = 'Adam'
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self.batch_size = 32
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self.replace_mse_with_huber_loss = False
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self.create_target_network = False
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class BCAgentParameters(AgentParameters):
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def __init__(self):
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super().__init__(algorithm=BCAlgorithmParameters(),
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exploration=EGreedyParameters(),
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memory=EpisodicExperienceReplayParameters(),
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networks={"main": BCNetworkParameters()})
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@property
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def path(self):
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return 'rl_coach.agents.bc_agent:BCAgent'
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# Behavioral Cloning Agent
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class BCAgent(ImitationAgent):
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def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
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super().__init__(agent_parameters, parent)
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def learn_from_batch(self, batch):
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network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
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# When using a policy head, the targets refer to the advantages that we are normally feeding the head with.
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# In this case, we need the policy head to just predict probabilities, so while we usually train the network
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# with log(Pi)*Advantages, in this specific case we will train it to log(Pi), which after the softmax will
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# predict Pi (=probabilities)
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targets = np.ones(batch.actions().shape[0])
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result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys),
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'output_0_0': batch.actions()},
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targets)
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total_loss, losses, unclipped_grads = result[:3]
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return total_loss, losses, unclipped_grads
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