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127 lines
6.1 KiB
Python
127 lines
6.1 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.core_types import RunPhase, ActionType
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from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
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from rl_coach.exploration_policies.exploration_policy import ExplorationParameters, ExplorationPolicy
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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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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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"""
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e-greedy is an exploration policy that is intended for both discrete and continuous action spaces.
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For discrete action spaces, it assumes that each action is assigned a value, and it selects the action with the
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highest value with probability 1 - epsilon. Otherwise, it selects a action sampled uniformly out of all the
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possible actions. The epsilon value is given by the user and can be given as a schedule.
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In evaluation, a different epsilon value can be specified.
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For continuous action spaces, it assumes that the mean action is given by the agent. With probability epsilon,
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it samples a random action out of the action space bounds. Otherwise, it selects the action according to a
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given continuous exploration policy, which is set to AdditiveNoise by default. In evaluation, the action is
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always selected according to the given continuous exploration policy (where its phase is set to evaluation as well).
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"""
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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, List[float]):
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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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if self.current_random_value < epsilon:
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chosen_action = self.action_space.sample()
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probabilities = np.full(len(self.action_space.actions),
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1. / (self.action_space.high[0] - self.action_space.low[0] + 1))
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else:
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chosen_action = np.argmax(action_values)
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# one-hot probabilities vector
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probabilities = np.zeros(len(self.action_space.actions))
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probabilities[chosen_action] = 1
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self.step_epsilon()
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return chosen_action, probabilities
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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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self.step_epsilon()
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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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def step_epsilon(self):
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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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