Source code for omnisafe.algorithms.on_policy.base.ppo

# Copyright 2023 OmniSafe Team. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Implementation of the PPO algorithm."""

from __future__ import annotations

import torch

from omnisafe.algorithms import registry
from omnisafe.algorithms.on_policy.base.policy_gradient import PolicyGradient


@registry.register
class PPO(PolicyGradient):
    """The Proximal Policy Optimization (PPO) algorithm.

    References:
        - Title: Proximal Policy Optimization Algorithms
        - Authors: John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov.
        - URL: `PPO <https://arxiv.org/abs/1707.06347>`_
    """

    def _loss_pi(
        self,
        obs: torch.Tensor,
        act: torch.Tensor,
        logp: torch.Tensor,
        adv: torch.Tensor,
    ) -> torch.Tensor:
        r"""Computing pi/actor loss.

        In Proximal Policy Optimization, the loss is defined as:

        .. math::

            L^{CLIP} = \underset{s_t \sim \rho_{\theta}}{\mathbb{E}} \left[
                \min ( r_t A^{R}_{\pi_{\theta}} (s_t, a_t) , \text{clip} (r_t, 1 - \epsilon, 1 + \epsilon)
                A^{R}_{\pi_{\theta}} (s_t, a_t)
            \right]

        where :math:`r_t = \frac{\pi_{\theta}^{'} (a_t|s_t)}{\pi_{\theta} (a_t|s_t)}`,
        :math:`\epsilon` is the clip parameter, and :math:`A^{R}_{\pi_{\theta}} (s_t, a_t)` is the
        advantage.

        Args:
            obs (torch.Tensor): The ``observation`` sampled from buffer.
            act (torch.Tensor): The ``action`` sampled from buffer.
            logp (torch.Tensor): The ``log probability`` of action sampled from buffer.
            adv (torch.Tensor): The ``advantage`` processed. ``reward_advantage`` here.

        Returns:
            The loss of pi/actor.
        """
        distribution = self._actor_critic.actor(obs)
        logp_ = self._actor_critic.actor.log_prob(act)
        std = self._actor_critic.actor.std
        ratio = torch.exp(logp_ - logp)
        ratio_cliped = torch.clamp(
            ratio,
            1 - self._cfgs.algo_cfgs.clip,
            1 + self._cfgs.algo_cfgs.clip,
        )
        loss = -torch.min(ratio * adv, ratio_cliped * adv).mean()
        loss -= self._cfgs.algo_cfgs.entropy_coef * distribution.entropy().mean()
        # useful extra info
        entropy = distribution.entropy().mean().item()
        self._logger.store(
            {
                'Train/Entropy': entropy,
                'Train/PolicyRatio': ratio,
                'Train/PolicyStd': std,
                'Loss/Loss_pi': loss.mean().item(),
            },
        )
        return loss