# Copyright 2023 OmniSafe Team. All Rights Reserved.
#
# 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
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# ==============================================================================
"""Model Predictive Control Planner of the Safe Actor Regularized Control algorithm."""
from __future__ import annotations
from typing import Any
import torch
from omnisafe.algorithms.model_based.base.ensemble import EnsembleDynamicsModel
from omnisafe.algorithms.model_based.planner.arc import ARCPlanner
from omnisafe.utils.config import Config
[docs]class SafeARCPlanner(ARCPlanner):
"""The planner of Safe Actor Regularized Control (ARC) algorithm.
References:
- Title: Learning Off-Policy with Online Planning
- Authors: Harshit Sikchi, Wenxuan Zhou, David Held.
- URL: `Safe ARC <https://arxiv.org/abs/2008.10066>`_
"""
def __init__( # pylint: disable=too-many-locals, too-many-arguments
self,
dynamics: EnsembleDynamicsModel,
planner_cfgs: Config,
gamma: float,
cost_gamma: float,
dynamics_state_shape: tuple[int, ...],
action_shape: tuple[int, ...],
action_max: float,
action_min: float,
device: torch.device,
**kwargs: Any,
) -> None:
"""Initializes the planner of Safe Actor Regularized Control (ARC) algorithm."""
super().__init__(
dynamics,
planner_cfgs,
gamma,
cost_gamma,
dynamics_state_shape,
action_shape,
action_max,
action_min,
device,
**kwargs,
)
self._cost_limit: float = kwargs['cost_limit']
self._cost_temperature: float = planner_cfgs.cost_temperature
[docs] @torch.no_grad()
def _update_mean_var(
self,
elite_actions: torch.Tensor,
elite_values: torch.Tensor,
info: dict[str, float],
) -> tuple[torch.Tensor, torch.Tensor]:
"""Update the mean and variance of the elite actions.
Args:
elite_actions (torch.Tensor): The elite actions.
elite_values (torch.Tensor): The elite values.
info (dict[str, float]): The dictionary containing the information of the elite values and actions.
Returns:
new_mean: The new mean of the elite actions.
new_var: The new variance of the elite actions.
"""
assert (
elite_actions.shape[0] == self._horizon
and elite_actions.shape[-1] == self._action_shape[0]
), 'Input elite_actions dimension should be equal to (self._horizon, self._num_elites, self._action_shape)'
assert (
elite_values.shape[-1] == 1
), 'Input elite_values dimension should be equal to (self._num_elites, 1)'
assert (
elite_actions.shape[1] == elite_values.shape[0]
), 'Number of action should be the same'
use_cost_temperature = info['Plan/feasible_num'] < self._num_elites
max_value = elite_values.max(0)[0]
if use_cost_temperature is True:
score = torch.exp(self._cost_temperature * (elite_values - max_value))
else:
score = torch.exp(self._temperature * (elite_values - max_value))
score /= score.sum(0)
new_mean = torch.sum(score.unsqueeze(0) * elite_actions, dim=1) / (score.sum(0) + 1e-9)
new_var = torch.sum(
score.unsqueeze(0) * (elite_actions - new_mean.unsqueeze(1)) ** 2,
dim=1,
) / (score.sum(0) + 1e-9)
new_var = new_var.clamp_(0, 2)
return new_mean, new_var
[docs] @torch.no_grad()
def _select_elites(
self,
actions: torch.Tensor,
traj: dict[str, torch.Tensor],
) -> tuple[torch.Tensor, torch.Tensor, dict[str, float]]:
"""Select elites from the sampled actions.
Args:
actions (torch.Tensor): Sampled actions.
traj (dict[str, torch.Tensor]): Trajectory dictionary.
Returns:
elites_value: The value of the elites.
elites_action: The action of the elites.
info: The dictionary containing the information of elites value and action.
"""
rewards = traj['rewards']
values = traj['values']
costs = traj['costs']
assert actions.shape == torch.Size(
[self._horizon, self._num_action, *self._action_shape],
# pylint: disable-next=line-too-long
), 'Input action dimension should be equal to (self._horizon, self._num_samples, self._action_shape)'
assert rewards.shape == torch.Size(
[self._horizon, self._num_models, int(self._num_particles * self._num_action), 1],
# pylint: disable-next=line-too-long
), 'Input rewards dimension should be equal to (self._horizon, self._num_models, self._num_particles*self._num_samples, 1)'
assert values.shape == torch.Size(
[self._horizon, self._num_models, int(self._num_particles * self._num_action), 1],
# pylint: disable-next=line-too-long
), 'Input values dimension should be equal to (self._horizon, self._num_models, self._num_particles*self._num_samples, 1)'
assert costs.shape == torch.Size(
[self._horizon, self._num_models, int(self._num_particles * self._num_action), 1],
# pylint: disable-next=line-too-long
), 'Input rewards dimension should be equal to (self._horizon, self._num_models, self._num_particles*self._num_samples, 1)'
costs = costs.reshape(
self._horizon,
self._num_models * self._num_particles,
self._num_action,
1,
)
max_cost = torch.max(costs, dim=1).values
sum_horizon_costs = torch.sum(max_cost, dim=0)
mean_episode_costs = sum_horizon_costs * (1000 / self._horizon)
rewards = rewards.reshape(
self._horizon,
self._num_models * self._num_particles,
self._num_action,
1,
)
values = values.reshape(
self._horizon,
self._num_models * self._num_particles,
self._num_action,
1,
)
sum_horizon_returns = torch.sum(rewards, dim=0) + values[-1, :, :, :]
mean_particles_returns = sum_horizon_returns.mean(dim=0)
mean_episode_returns = mean_particles_returns * (1000 / self._horizon)
assert mean_episode_returns.shape[0] == self._num_action
feasible_num = torch.sum(mean_episode_costs <= self._cost_limit).item()
if feasible_num < self._num_elites:
elite_values, elite_actions = -mean_episode_costs, actions
else:
elite_idxs = (
(mean_episode_costs <= self._cost_limit).nonzero().reshape(-1)
) # like tensor([0, 1])
elite_values, elite_actions = mean_episode_returns[elite_idxs], actions[:, elite_idxs]
info = {
'Plan/episode_returns_max': mean_episode_returns.max().item(),
'Plan/episode_returns_mean': mean_episode_returns.mean().item(),
'Plan/episode_returns_min': mean_episode_returns.min().item(),
'Plan/feasible_num': feasible_num,
'Plan/episode_costs_max': mean_episode_costs.max().item(),
'Plan/episode_costs_mean': mean_episode_costs.mean().item(),
'Plan/episode_costs_min': mean_episode_costs.min().item(),
}
return elite_values, elite_actions, info
[docs] @torch.no_grad()
def output_action(self, state: torch.Tensor) -> tuple[torch.Tensor, dict[str, float]]:
"""Output the action given the state.
Args:
state (torch.Tensor): State of the environment.
Returns:
action: The action of the agent.
info: The dictionary containing the information of the action.
"""
assert state.shape == torch.Size(
[1, *self._dynamics_state_shape],
), 'Input state dimension should be equal to (1, self._dynamics_state_shape)'
last_mean = torch.zeros_like(self._action_sequence_mean)
last_var = self._action_sequence_var.clone()
last_mean[:-1] = self._action_sequence_mean[1:].clone()
last_mean[-1] = self._action_sequence_mean[-1].clone()
current_iter = 0
actions_actor = self._act_from_actor(state)
info: dict[str, float] = {}
while current_iter < self._num_iterations and last_var.max() > self._epsilon:
actions_gauss = self._act_from_last_gaus(last_mean=last_mean, last_var=last_var)
actions = torch.cat([actions_gauss, actions_actor], dim=1)
# [horizon, num_sample, action_shape]
states_repeat, actions_repeat = self._state_action_repeat(state, actions)
# pylint: disable-next=line-too-long
# [num_particles * num_samples/num_ensemble, state_shape], [horizon, num_particles * num_samples/num_ensemble, action_shape]
traj = self._dynamics.imagine(states_repeat, self._horizon, actions_repeat)
# pylint: disable-next=line-too-long
# {states, rewards, values}, each value shape is [horizon, num_ensemble, num_particles * num_samples/num_ensemble, 1]
elite_values, elite_actions, info = self._select_elites(actions, traj)
# [num_sample, 1]
new_mean, new_var = self._update_mean_var(
elite_actions,
elite_values,
info,
)
# last_mean = self._momentum * last_mean + (1 - self._momentum) * new_mean
last_mean = new_mean
last_var = self._momentum * last_var + (1 - self._momentum) * new_var
current_iter += 1
logger_info = {
'Plan/iter': current_iter,
'Plan/last_var_mean': last_var.mean().item(),
'Plan/last_var_max': last_var.max().item(),
'Plan/last_var_min': last_var.min().item(),
}
logger_info.update(info)
self._action_sequence_mean = last_mean.clone()
return last_mean[0].clone().unsqueeze(0), logger_info