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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 DiscreteActionExplorationPolicy, ExplorationParameters
from rl_coach.spaces import ActionSpace

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

[docs]class Categorical(DiscreteActionExplorationPolicy): """ 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, List[float]): if self.phase == RunPhase.TRAIN: # choose actions according to the probabilities action = np.random.choice(self.action_space.actions, p=action_values) return action, action_values else: # take the action with the highest probability action = np.argmax(action_values) one_hot_action_probabilities = np.zeros(len(self.action_space.actions)) one_hot_action_probabilities[action] = 1 return action, one_hot_action_probabilities def get_control_param(self): return 0