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6d40ad1650
* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
55 lines
2.0 KiB
Python
55 lines
2.0 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.exploration_policy import ExplorationPolicy, ExplorationParameters
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from rl_coach.spaces import ActionSpace
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class CategoricalParameters(ExplorationParameters):
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@property
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def path(self):
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return 'rl_coach.exploration_policies.categorical:Categorical'
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class Categorical(ExplorationPolicy):
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"""
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Categorical exploration policy is intended for discrete action spaces. It expects the action values to
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represent a probability distribution over the action, from which a single action will be sampled.
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In evaluation, the action that has the highest probability will be selected. This is particularly useful for
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actor-critic schemes, where the actors output is a probability distribution over the actions.
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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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super().__init__(action_space)
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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if self.phase == RunPhase.TRAIN:
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# choose actions according to the probabilities
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return np.random.choice(self.action_space.actions, p=action_values)
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else:
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# take the action with the highest probability
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return np.argmax(action_values)
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def get_control_param(self):
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return 0
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