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95 lines
4.2 KiB
Python
95 lines
4.2 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 typing import Union
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import numpy as np
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from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters
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from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay, \
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EpisodicExperienceReplayParameters
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class PALAlgorithmParameters(DQNAlgorithmParameters):
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def __init__(self):
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super().__init__()
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self.pal_alpha = 0.9
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self.persistent_advantage_learning = False
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self.monte_carlo_mixing_rate = 0.1
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class PALAgentParameters(DQNAgentParameters):
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def __init__(self):
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super().__init__()
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self.algorithm = PALAlgorithmParameters()
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self.memory = EpisodicExperienceReplayParameters()
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@property
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def path(self):
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return 'rl_coach.agents.pal_agent:PALAgent'
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# Persistent Advantage Learning - https://arxiv.org/pdf/1512.04860.pdf
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class PALAgent(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.alpha = agent_parameters.algorithm.pal_alpha
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self.persistent = agent_parameters.algorithm.persistent_advantage_learning
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self.monte_carlo_mixing_rate = agent_parameters.algorithm.monte_carlo_mixing_rate
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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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# next state values
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q_st_plus_1_target, q_st_plus_1_online = self.networks['main'].parallel_prediction([
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(self.networks['main'].target_network, batch.next_states(network_keys)),
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(self.networks['main'].online_network, batch.next_states(network_keys))
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])
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selected_actions = np.argmax(q_st_plus_1_online, 1)
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v_st_plus_1_target = np.max(q_st_plus_1_target, 1)
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# current state values
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q_st_target, q_st_online = self.networks['main'].parallel_prediction([
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(self.networks['main'].target_network, batch.states(network_keys)),
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(self.networks['main'].online_network, batch.states(network_keys))
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])
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v_st_target = np.max(q_st_target, 1)
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# calculate TD error
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TD_targets = np.copy(q_st_online)
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for i in range(self.ap.network_wrappers['main'].batch_size):
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TD_targets[i, batch.actions()[i]] = batch.rewards()[i] + \
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(1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
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q_st_plus_1_target[i][selected_actions[i]]
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advantage_learning_update = v_st_target[i] - q_st_target[i, batch.actions()[i]]
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next_advantage_learning_update = v_st_plus_1_target[i] - q_st_plus_1_target[i, selected_actions[i]]
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# Persistent Advantage Learning or Regular Advantage Learning
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if self.persistent:
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TD_targets[i, batch.actions()[i]] -= self.alpha * min(advantage_learning_update, next_advantage_learning_update)
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else:
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TD_targets[i, batch.actions()[i]] -= self.alpha * advantage_learning_update
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# mixing monte carlo updates
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monte_carlo_target = batch.total_returns()[i]
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TD_targets[i, batch.actions()[i]] = (1 - self.monte_carlo_mixing_rate) * TD_targets[i, batch.actions()[i]] \
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+ self.monte_carlo_mixing_rate * monte_carlo_target
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result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), 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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