boltzmann.py

#
# 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.schedules import Schedule
from rl_coach.spaces import ActionSpace


class BoltzmannParameters(ExplorationParameters):
    def __init__(self):
        super().__init__()
        self.temperature_schedule = None

    @property
    def path(self):
        return 'rl_coach.exploration_policies.boltzmann:Boltzmann'



class Boltzmann(ExplorationPolicy):
    def __init__(self, action_space: ActionSpace, temperature_schedule: Schedule):
        """
        :param action_space: the action space used by the environment
        :param temperature_schedule: the schedule for the temperature parameter of the softmax
        """
        super().__init__(action_space)
        self.temperature_schedule = temperature_schedule

    def get_action(self, action_values: List[ActionType]) -> ActionType:
        if self.phase == RunPhase.TRAIN:
            self.temperature_schedule.step()
        # softmax calculation
        exp_probabilities = np.exp(action_values / self.temperature_schedule.current_value)
        probabilities = exp_probabilities / np.sum(exp_probabilities)
        # make sure probs sum to 1
        probabilities[-1] = 1 - np.sum(probabilities[:-1])
        # choose actions according to the probabilities
        return np.random.choice(range(self.action_space.shape), p=probabilities)

    def get_control_param(self):
        return self.temperature_schedule.current_value