mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
6d40ad1650
* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
115 lines
4.9 KiB
Python
115 lines
4.9 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 Union
|
|
|
|
import numpy as np
|
|
|
|
from rl_coach.agents.ddpg_agent import DDPGAgent, DDPGAgentParameters, DDPGAlgorithmParameters
|
|
from rl_coach.core_types import RunPhase
|
|
from rl_coach.spaces import SpacesDefinition
|
|
|
|
|
|
class HACDDPGAlgorithmParameters(DDPGAlgorithmParameters):
|
|
"""
|
|
:param time_limit: (int)
|
|
The number of steps the agent is allowed to act for while trying to achieve its goal
|
|
|
|
:param sub_goal_testing_rate: (float)
|
|
The percent of episodes that will be used for testing the sub goals generated by the upper level agents.
|
|
"""
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.time_limit = 40
|
|
self.sub_goal_testing_rate = 0.5
|
|
|
|
|
|
class HACDDPGAgentParameters(DDPGAgentParameters):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.algorithm = HACDDPGAlgorithmParameters()
|
|
|
|
@property
|
|
def path(self):
|
|
return 'rl_coach.agents.hac_ddpg_agent:HACDDPGAgent'
|
|
|
|
|
|
# Hierarchical Actor Critic Generating Subgoals DDPG Agent - https://arxiv.org/pdf/1712.00948.pdf
|
|
class HACDDPGAgent(DDPGAgent):
|
|
def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
|
|
super().__init__(agent_parameters, parent)
|
|
self.sub_goal_testing_rate = self.ap.algorithm.sub_goal_testing_rate
|
|
self.graph_manager = None
|
|
|
|
def choose_action(self, curr_state):
|
|
# top level decides, for each of his generated sub-goals, for all the layers beneath him if this is a sub-goal
|
|
# testing phase
|
|
|
|
graph_manager = self.parent_level_manager.parent_graph_manager
|
|
if self.ap.is_a_highest_level_agent:
|
|
graph_manager.should_test_current_sub_goal = np.random.rand() < self.sub_goal_testing_rate
|
|
|
|
if self.phase == RunPhase.TRAIN:
|
|
if graph_manager.should_test_current_sub_goal:
|
|
self.exploration_policy.change_phase(RunPhase.TEST)
|
|
else:
|
|
self.exploration_policy.change_phase(self.phase)
|
|
|
|
action_info = super().choose_action(curr_state)
|
|
return action_info
|
|
|
|
def update_transition_before_adding_to_replay_buffer(self, transition):
|
|
graph_manager = self.parent_level_manager.parent_graph_manager
|
|
|
|
# deal with goals given from a higher level agent
|
|
if not self.ap.is_a_highest_level_agent:
|
|
transition.state['desired_goal'] = self.current_hrl_goal
|
|
transition.next_state['desired_goal'] = self.current_hrl_goal
|
|
# TODO: allow setting goals which are not part of the state. e.g. state-embedding using get_prediction
|
|
self.distance_from_goal.add_sample(self.spaces.goal.distance_from_goal(
|
|
self.current_hrl_goal, transition.next_state))
|
|
goal_reward, sub_goal_reached = self.spaces.goal.get_reward_for_goal_and_state(
|
|
self.current_hrl_goal, transition.next_state)
|
|
transition.reward = goal_reward
|
|
transition.game_over = transition.game_over or sub_goal_reached
|
|
|
|
# each level tests its own generated sub goals
|
|
if not self.ap.is_a_lowest_level_agent and graph_manager.should_test_current_sub_goal:
|
|
#TODO-fixme
|
|
# _, sub_goal_reached = self.parent_level_manager.environment.agents['agent_1'].spaces.goal.\
|
|
# get_reward_for_goal_and_state(transition.action, transition.next_state)
|
|
|
|
_, sub_goal_reached = self.spaces.goal.get_reward_for_goal_and_state(
|
|
transition.action, transition.next_state)
|
|
|
|
sub_goal_is_missed = not sub_goal_reached
|
|
|
|
if sub_goal_is_missed:
|
|
transition.reward = -self.ap.algorithm.time_limit
|
|
return transition
|
|
|
|
def set_environment_parameters(self, spaces: SpacesDefinition):
|
|
super().set_environment_parameters(spaces)
|
|
|
|
if self.ap.is_a_highest_level_agent:
|
|
# the rest of the levels already have an in_action_space set to be of type GoalsSpace, thus they will have
|
|
# their GoalsSpace set to the in_action_space in agent.set_environment_parameters()
|
|
self.spaces.goal = self.spaces.action
|
|
self.spaces.goal.set_target_space(self.spaces.state[self.spaces.goal.goal_name])
|
|
|
|
if not self.ap.is_a_highest_level_agent:
|
|
self.spaces.reward.reward_success_threshold = self.spaces.goal.reward_type.goal_reaching_reward
|