# Copyright 2023 OmniSafe Team. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Model Predictive Control Planner of the Constrained Cross-Entropy 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.cem import CEMPlanner
from omnisafe.utils.config import Config
[docs]class CCEPlanner(CEMPlanner):
"""The planner of Constrained Cross-Entropy (CCE) algorithm.
References:
- Title: Constrained Cross-Entropy Method for Safe Reinforcement Learning
- Authors: Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess,
Tom Erez, Yuval Tassa, David Silver, Daan Wierstra.
- URL: `CCE <https://proceedings.neurips.cc/paper/2018/hash/34ffeb359a192eb8174b6854643cc046-Abstract.html>`_
"""
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 Constrained Cross-Entropy (CCE) 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']
[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']
costs = traj['costs']
assert actions.shape == torch.Size( # pylint: disable=line-too-long
[self._horizon, self._num_samples, *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( # pylint: disable=line-too-long
[
self._horizon,
self._num_models,
int(self._num_particles / self._num_models * self._num_samples),
1,
],
# pylint: disable-next=line-too-long
), 'Input rewards dimension should be equal to (self._horizon, self._num_models, self._num_particles/self._num_models*self._num_samples, 1)'
assert costs.shape == torch.Size( # pylint: disable=line-too-long
[
self._horizon,
self._num_models,
int(self._num_particles / self._num_models * self._num_samples),
1,
],
# pylint: disable-next=line-too-long
), 'Input rewards dimension should be equal to (self._horizon, self._num_models, self._num_particles/self._num_models*self._num_samples, 1)'
costs = costs.reshape(self._horizon, self._num_particles, self._num_samples, 1)
sum_horizon_costs = torch.sum(costs, dim=0)
mean_particles_costs = sum_horizon_costs.mean(dim=0)
mean_episode_costs = mean_particles_costs * (1000 / self._horizon)
returns = rewards.reshape(self._horizon, self._num_particles, self._num_samples, 1)
sum_horizon_returns = torch.sum(returns, dim=0)
mean_particles_returns = sum_horizon_returns.mean(dim=0)
mean_episode_returns = mean_particles_returns * (1000 / self._horizon)
assert mean_particles_returns.shape[0] == self._num_samples
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]
elite_idxs_topk = torch.topk(elite_values.squeeze(1), self._num_elites, dim=0).indices
elite_returns_topk, elite_actions_topk = (
elite_values[elite_idxs_topk],
elite_actions[:, elite_idxs_topk],
)
info = {
'Plan/feasible_num': feasible_num,
'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/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_returns_topk, elite_actions_topk, info