mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
113 lines
5.0 KiB
Python
113 lines
5.0 KiB
Python
#
|
|
# 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 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):
|
|
super().__init__()
|
|
self.noise_schedule = LinearSchedule(0.1, 0.1, 50000)
|
|
self.evaluation_noise = 0.05
|
|
self.noise_as_percentage_from_action_space = True
|
|
|
|
@property
|
|
def path(self):
|
|
return 'rl_coach.exploration_policies.additive_noise:AdditiveNoise'
|
|
|
|
|
|
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):
|
|
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
|