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* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter
123 lines
4.5 KiB
Python
123 lines
4.5 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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from rl_coach.base_parameters import Parameters
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from rl_coach.core_types import RunPhase, ActionType
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from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace, GoalsSpace
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class ExplorationParameters(Parameters):
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def __init__(self):
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self.action_space = None
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@property
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def path(self):
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return 'rl_coach.exploration_policies.exploration_policy:ExplorationPolicy'
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class ExplorationPolicy(object):
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"""
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An exploration policy takes the predicted actions or action values from the agent, and selects the action to
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actually apply to the environment using some predefined algorithm.
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"""
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def __init__(self, action_space: ActionSpace):
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"""
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:param action_space: the action space used by the environment
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"""
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self.phase = RunPhase.HEATUP
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self.action_space = action_space
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def reset(self):
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"""
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Used for resetting the exploration policy parameters when needed
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:return: None
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"""
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pass
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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"""
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Given a list of values corresponding to each action,
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choose one actions according to the exploration policy
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:param action_values: A list of action values
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:return: The chosen action,
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The probability of the action (if available, otherwise 1 for absolute certainty in the action)
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"""
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raise NotImplementedError()
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def change_phase(self, phase):
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"""
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Change between running phases of the algorithm
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:param phase: Either Heatup or Train
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:return: none
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"""
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self.phase = phase
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def requires_action_values(self) -> bool:
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"""
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Allows exploration policies to define if they require the action values for the current step.
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This can save up a lot of computation. For example in e-greedy, if the random value generated is smaller
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than epsilon, the action is completely random, and the action values don't need to be calculated
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:return: True if the action values are required. False otherwise
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"""
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return True
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def get_control_param(self):
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return 0
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class DiscreteActionExplorationPolicy(ExplorationPolicy):
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"""
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A discrete action exploration policy.
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"""
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def __init__(self, action_space: ActionSpace):
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"""
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:param action_space: the action space used by the environment
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"""
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assert isinstance(action_space, DiscreteActionSpace)
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super().__init__(action_space)
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def get_action(self, action_values: List[ActionType]) -> (ActionType, List):
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"""
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Given a list of values corresponding to each action,
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choose one actions according to the exploration policy
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:param action_values: A list of action values
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:return: The chosen action,
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The probabilities of actions to select from (if not available a one-hot vector)
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"""
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if self.__class__ == ExplorationPolicy:
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raise ValueError("The ExplorationPolicy class is an abstract class and should not be used directly. "
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"Please set the exploration parameters to point to an inheriting class like EGreedy or "
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"AdditiveNoise")
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else:
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raise ValueError("The get_action function should be overridden in the inheriting exploration class")
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class ContinuousActionExplorationPolicy(ExplorationPolicy):
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"""
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A continuous action exploration policy.
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"""
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def __init__(self, action_space: ActionSpace):
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"""
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:param action_space: the action space used by the environment
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"""
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assert isinstance(action_space, BoxActionSpace) or \
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(hasattr(action_space, 'filtered_action_space') and
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isinstance(action_space.filtered_action_space, BoxActionSpace)) or \
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isinstance(action_space, GoalsSpace)
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super().__init__(action_space)
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