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129 lines
5.4 KiB
Python
129 lines
5.4 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.value_optimization_agent import ValueOptimizationAgent
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from rl_coach.architectures.tensorflow_components.heads.naf_head import NAFHeadParameters
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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 AlgorithmParameters, AgentParameters, \
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NetworkParameters
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from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
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from rl_coach.core_types import ActionInfo, EnvironmentSteps
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from rl_coach.exploration_policies.ou_process import OUProcessParameters
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
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from rl_coach.spaces import BoxActionSpace
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class NAFNetworkParameters(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()
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self.heads_parameters = [NAFHeadParameters()]
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self.loss_weights = [1.0]
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self.optimizer_type = 'Adam'
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self.learning_rate = 0.001
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self.async_training = True
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self.create_target_network = True
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class NAFAlgorithmParameters(AlgorithmParameters):
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def __init__(self):
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super().__init__()
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self.num_consecutive_training_steps = 5
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self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
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self.rate_for_copying_weights_to_target = 0.001
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class NAFAgentParameters(AgentParameters):
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def __init__(self):
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super().__init__(algorithm=NAFAlgorithmParameters(),
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exploration=OUProcessParameters(),
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memory=EpisodicExperienceReplayParameters(),
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networks={"main": NAFNetworkParameters()})
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@property
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def path(self):
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return 'rl_coach.agents.naf_agent:NAFAgent'
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# Normalized Advantage Functions - https://arxiv.org/pdf/1603.00748.pdf
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class NAFAgent(ValueOptimizationAgent):
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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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self.l_values = self.register_signal("L")
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self.a_values = self.register_signal("Advantage")
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self.mu_values = self.register_signal("Action")
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self.v_values = self.register_signal("V")
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self.TD_targets = self.register_signal("TD targets")
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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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# TD error = r + discount*v_st_plus_1 - q_st
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v_st_plus_1 = self.networks['main'].target_network.predict(
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batch.next_states(network_keys),
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self.networks['main'].target_network.output_heads[0].V,
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squeeze_output=False,
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)
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TD_targets = np.expand_dims(batch.rewards(), -1) + \
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(1.0 - np.expand_dims(batch.game_overs(), -1)) * self.ap.algorithm.discount * v_st_plus_1
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self.TD_targets.add_sample(TD_targets)
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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(len(batch.actions().shape) == 1)
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}, TD_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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def choose_action(self, curr_state):
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if type(self.spaces.action) != BoxActionSpace:
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raise ValueError('NAF works only for continuous control problems')
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# convert to batch so we can run it through the network
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tf_input_state = self.prepare_batch_for_inference(curr_state, 'main')
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naf_head = self.networks['main'].online_network.output_heads[0]
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action_values = self.networks['main'].online_network.predict(tf_input_state, outputs=naf_head.mu,
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squeeze_output=False)
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# get the actual action to use
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action = self.exploration_policy.get_action(action_values)
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# get the internal values for logging
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outputs = [naf_head.mu, naf_head.Q, naf_head.L, naf_head.A, naf_head.V]
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result = self.networks['main'].online_network.predict(
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{**tf_input_state, 'output_0_0': action_values},
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outputs=outputs
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)
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mu, Q, L, A, V = result
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# store the q values statistics for logging
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self.q_values.add_sample(Q)
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self.l_values.add_sample(L)
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self.a_values.add_sample(A)
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self.mu_values.add_sample(mu)
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self.v_values.add_sample(V)
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action_info = ActionInfo(action=action, action_value=Q)
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return action_info
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