#
# Copyright (c) 2017 Intel Corporation
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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from typing import List
from rl_coach.base_parameters import Parameters
from rl_coach.core_types import RunPhase, ActionType
from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace, GoalsSpace
class ExplorationParameters(Parameters):
def __init__(self):
self.action_space = None
@property
def path(self):
return 'rl_coach.exploration_policies.exploration_policy:ExplorationPolicy'
[docs]class ExplorationPolicy(object):
"""
An exploration policy takes the predicted actions or action values from the agent, and selects the action to
actually apply to the environment using some predefined algorithm.
"""
def __init__(self, action_space: ActionSpace):
"""
:param action_space: the action space used by the environment
"""
self.phase = RunPhase.HEATUP
self.action_space = action_space
[docs] def reset(self):
"""
Used for resetting the exploration policy parameters when needed
:return: None
"""
pass
[docs] def get_action(self, action_values: List[ActionType]) -> ActionType:
"""
Given a list of values corresponding to each action,
choose one actions according to the exploration policy
:param action_values: A list of action values
:return: The chosen action,
The probability of the action (if available, otherwise 1 for absolute certainty in the action)
"""
raise NotImplementedError()
[docs] def change_phase(self, phase):
"""
Change between running phases of the algorithm
:param phase: Either Heatup or Train
:return: none
"""
self.phase = phase
[docs] def requires_action_values(self) -> bool:
"""
Allows exploration policies to define if they require the action values for the current step.
This can save up a lot of computation. For example in e-greedy, if the random value generated is smaller
than epsilon, the action is completely random, and the action values don't need to be calculated
:return: True if the action values are required. False otherwise
"""
return True
def get_control_param(self):
return 0
class DiscreteActionExplorationPolicy(ExplorationPolicy):
"""
A discrete action exploration policy.
"""
def __init__(self, action_space: ActionSpace):
"""
:param action_space: the action space used by the environment
"""
assert isinstance(action_space, DiscreteActionSpace)
super().__init__(action_space)
def get_action(self, action_values: List[ActionType]) -> (ActionType, List):
"""
Given a list of values corresponding to each action,
choose one actions according to the exploration policy
:param action_values: A list of action values
:return: The chosen action,
The probabilities of actions to select from (if not available a one-hot vector)
"""
if self.__class__ == ExplorationPolicy:
raise ValueError("The ExplorationPolicy class is an abstract class and should not be used directly. "
"Please set the exploration parameters to point to an inheriting class like EGreedy or "
"AdditiveNoise")
else:
raise ValueError("The get_action function should be overridden in the inheriting exploration class")
class ContinuousActionExplorationPolicy(ExplorationPolicy):
"""
A continuous action exploration policy.
"""
def __init__(self, action_space: ActionSpace):
"""
:param action_space: the action space used by the environment
"""
assert isinstance(action_space, BoxActionSpace) or \
(hasattr(action_space, 'filtered_action_space') and
isinstance(action_space.filtered_action_space, BoxActionSpace)) or \
isinstance(action_space, GoalsSpace)
super().__init__(action_space)