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103 lines
4.7 KiB
Python
103 lines
4.7 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
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import numpy as np
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from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
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from rl_coach.schedules import Schedule, LinearSchedule
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from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace
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from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
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from rl_coach.core_types import RunPhase, ActionType
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from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
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from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy
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class EGreedyParameters(ExplorationParameters):
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def __init__(self):
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super().__init__()
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self.epsilon_schedule = LinearSchedule(0.5, 0.01, 50000)
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self.evaluation_epsilon = 0.05
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self.continuous_exploration_policy_parameters = AdditiveNoiseParameters()
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self.continuous_exploration_policy_parameters.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
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# for continuous control -
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# (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)
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@property
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def path(self):
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return 'rl_coach.exploration_policies.e_greedy:EGreedy'
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class EGreedy(ExplorationPolicy):
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def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule,
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evaluation_epsilon: float,
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continuous_exploration_policy_parameters: ExplorationParameters=AdditiveNoiseParameters()):
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"""
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:param action_space: the action space used by the environment
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:param epsilon_schedule: a schedule for the epsilon values
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:param evaluation_epsilon: the epsilon value to use for evaluation phases
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:param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
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if the e-greedy is used for a continuous policy
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"""
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super().__init__(action_space)
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self.epsilon_schedule = epsilon_schedule
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self.evaluation_epsilon = evaluation_epsilon
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if isinstance(self.action_space, BoxActionSpace):
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# for continuous e-greedy (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)
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continuous_exploration_policy_parameters.action_space = action_space
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self.continuous_exploration_policy = \
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dynamic_import_and_instantiate_module_from_params(continuous_exploration_policy_parameters)
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self.current_random_value = np.random.rand()
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def requires_action_values(self):
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epsilon = self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
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return self.current_random_value >= epsilon
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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epsilon = self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
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if isinstance(self.action_space, DiscreteActionSpace):
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top_action = np.argmax(action_values)
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if self.current_random_value < epsilon:
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chosen_action = self.action_space.sample()
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else:
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chosen_action = top_action
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else:
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if self.current_random_value < epsilon and self.phase == RunPhase.TRAIN:
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chosen_action = self.action_space.sample()
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else:
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chosen_action = self.continuous_exploration_policy.get_action(action_values)
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# step the epsilon schedule and generate a new random value for next time
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if self.phase == RunPhase.TRAIN:
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self.epsilon_schedule.step()
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self.current_random_value = np.random.rand()
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return chosen_action
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def get_control_param(self):
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if isinstance(self.action_space, DiscreteActionSpace):
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return self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
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elif isinstance(self.action_space, BoxActionSpace):
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return self.continuous_exploration_policy.get_control_param()
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def change_phase(self, phase):
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super().change_phase(phase)
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if isinstance(self.action_space, BoxActionSpace):
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self.continuous_exploration_policy.change_phase(phase)
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