Source code for rl_coach.exploration_policies.additive_noise

# 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 ContinuousActionExplorationPolicy, ExplorationParameters
from rl_coach.schedules import Schedule, LinearSchedule
from rl_coach.spaces import ActionSpace, BoxActionSpace

# TODO: consider renaming to gaussian sampling

class AdditiveNoiseParameters(ExplorationParameters):
    def __init__(self):
        self.noise_schedule = LinearSchedule(0.1, 0.1, 50000)
        self.evaluation_noise = 0.05
        self.noise_as_percentage_from_action_space = True

    def path(self):
        return 'rl_coach.exploration_policies.additive_noise:AdditiveNoise'

[docs]class AdditiveNoise(ContinuousActionExplorationPolicy): """ AdditiveNoise is an exploration policy intended for continuous action spaces. It takes the action from the agent and adds a Gaussian distributed noise to it. The amount of noise added to the action follows the noise amount that can be given in two different ways: 1. Specified by the user as a noise schedule which is taken in percentiles out of the action space size 2. Specified by the agents action. In case the agents action is a list with 2 values, the 1st one is assumed to be the mean of the action, and 2nd is assumed to be its standard deviation. """ def __init__(self, action_space: ActionSpace, noise_schedule: Schedule, evaluation_noise: float, noise_as_percentage_from_action_space: bool = True): """ :param action_space: the action space used by the environment :param noise_schedule: the schedule for the noise :param evaluation_noise: the noise variance that will be used during evaluation phases :param noise_as_percentage_from_action_space: a bool deciding whether the noise is absolute or as a percentage from the action space """ super().__init__(action_space) self.noise_schedule = noise_schedule self.evaluation_noise = evaluation_noise self.noise_as_percentage_from_action_space = noise_as_percentage_from_action_space if not isinstance(action_space, BoxActionSpace) and \ (hasattr(action_space, 'filtered_action_space') and not isinstance(action_space.filtered_action_space, BoxActionSpace)): raise ValueError("Additive noise exploration works only for continuous controls." "The given action space is of type: {}".format(action_space.__class__.__name__)) if not np.all(-np.inf < action_space.high) or not np.all(action_space.high < np.inf)\ or not np.all(-np.inf < action_space.low) or not np.all(action_space.low < np.inf): raise ValueError("Additive noise exploration requires bounded actions") def get_action(self, action_values: List[ActionType]) -> ActionType: # TODO-potential-bug consider separating internally defined stdev and externally defined stdev into 2 policies # set the current noise if self.phase == RunPhase.TEST: current_noise = self.evaluation_noise else: current_noise = self.noise_schedule.current_value # scale the noise to the action space range if self.noise_as_percentage_from_action_space: action_values_std = current_noise * (self.action_space.high - self.action_space.low) else: action_values_std = current_noise # extract the mean values if isinstance(action_values, list): # the action values are expected to be a list with the action mean and optionally the action stdev action_values_mean = action_values[0].squeeze() else: # the action values are expected to be a numpy array representing the action mean action_values_mean = action_values.squeeze() # step the noise schedule if self.phase is not RunPhase.TEST: self.noise_schedule.step() # the second element of the list is assumed to be the standard deviation if isinstance(action_values, list) and len(action_values) > 1: action_values_std = action_values[1].squeeze() # add noise to the action means if self.phase is not RunPhase.TEST: action = np.random.normal(action_values_mean, action_values_std) else: action = action_values_mean return np.atleast_1d(action) def get_control_param(self): return np.ones(self.action_space.shape)*self.noise_schedule.current_value