# rl_coach.exploration_policies.categorical — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.exploration_policies.categorical

    #
    # Copyright (c) 2017 Intel Corporation
    #
    # 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.
    #

    from typing import List

    import numpy as np

    from rl_coach.core_types import RunPhase, ActionType
    from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
    from rl_coach.spaces import ActionSpace

    class CategoricalParameters(ExplorationParameters):
        @property
        def path(self):
            return 'rl_coach.exploration_policies.categorical:Categorical'

    class Categorical(ExplorationPolicy):
        """
        Categorical exploration policy is intended for discrete action spaces. It expects the action values to
        represent a probability distribution over the action, from which a single action will be sampled.
        In evaluation, the action that has the highest probability will be selected. This is particularly useful for
        actor-critic schemes, where the actors output is a probability distribution over the actions.
        """
        def __init__(self, action_space: ActionSpace):
            """
            :param action_space: the action space used by the environment
            """
            super().__init__(action_space)

        def get_action(self, action_values: List[ActionType]) -> ActionType:
            if self.phase == RunPhase.TRAIN:
                # choose actions according to the probabilities
                return np.random.choice(self.action_space.actions, p=action_values)
            else:
                # take the action with the highest probability
                return np.argmax(action_values)

        def get_control_param(self):
            return 0