mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
84 lines
3.5 KiB
Python
84 lines
3.5 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, EnvironmentSteps
|
|
from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
|
|
from rl_coach.exploration_policies.e_greedy import EGreedy, EGreedyParameters
|
|
from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
|
|
from rl_coach.schedules import Schedule, LinearSchedule, PieceWiseSchedule
|
|
from rl_coach.spaces import ActionSpace
|
|
|
|
|
|
class UCBParameters(EGreedyParameters):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.architecture_num_q_heads = 10
|
|
self.bootstrapped_data_sharing_probability = 1.0
|
|
self.epsilon_schedule = PieceWiseSchedule([
|
|
(LinearSchedule(1, 0.1, 1000000), EnvironmentSteps(1000000)),
|
|
(LinearSchedule(0.1, 0.01, 4000000), EnvironmentSteps(4000000))
|
|
])
|
|
self.lamb = 0.1
|
|
|
|
@property
|
|
def path(self):
|
|
return 'rl_coach.exploration_policies.ucb:UCB'
|
|
|
|
|
|
class UCB(EGreedy):
|
|
def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule, evaluation_epsilon: float,
|
|
architecture_num_q_heads: int, lamb: int,
|
|
continuous_exploration_policy_parameters: ExplorationParameters = AdditiveNoiseParameters()):
|
|
"""
|
|
:param action_space: the action space used by the environment
|
|
:param epsilon_schedule: a schedule for the epsilon values
|
|
:param evaluation_epsilon: the epsilon value to use for evaluation phases
|
|
:param architecture_num_q_heads: the number of q heads to select from
|
|
:param lamb: lambda coefficient for taking the standard deviation into account
|
|
:param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
|
|
if the e-greedy is used for a continuous policy
|
|
"""
|
|
super().__init__(action_space, epsilon_schedule, evaluation_epsilon, continuous_exploration_policy_parameters)
|
|
self.num_heads = architecture_num_q_heads
|
|
self.lamb = lamb
|
|
self.std = 0
|
|
self.last_action_values = 0
|
|
|
|
def select_head(self):
|
|
pass
|
|
|
|
def get_action(self, action_values: List[ActionType]) -> ActionType:
|
|
# action values are none in case the exploration policy is going to select a random action
|
|
if action_values is not None:
|
|
if self.requires_action_values():
|
|
mean = np.mean(action_values, axis=0)
|
|
if self.phase == RunPhase.TRAIN:
|
|
self.std = np.std(action_values, axis=0)
|
|
self.last_action_values = mean + self.lamb * self.std
|
|
else:
|
|
self.last_action_values = mean
|
|
return super().get_action(self.last_action_values)
|
|
|
|
def get_control_param(self):
|
|
if self.phase == RunPhase.TRAIN:
|
|
return np.mean(self.std)
|
|
else:
|
|
return 0
|