# 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,
# 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 DiscreteActionExplorationPolicy, ExplorationParameters
from rl_coach.spaces import ActionSpace
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
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
# 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