LeonShangguan

从stable-baseline3的源代码来看主要是

if entropy is not  None:
    entropy_loss = -th.mean(entropy)

进一步找代码可以看到，实际上返回的entropy是

        distribution = self._get_action_dist_from_latent(latent_pi, latent_sde)
        return distribution.entropy()

注意，其中的latent_pi和latent_sde是指：

也就是两个变量之间的熵

def _get_action_dist_from_latent(self, latent_pi: th.Tensor, latent_sde: Optional[th.Tensor] = None) -> Distribution:
        """
        Retrieve action distribution given the latent codes.

        :param latent_pi: Latent code for the actor
        :param latent_sde: Latent code for the gSDE exploration function
        :return: Action distribution
        """
        mean_actions = self.action_net(latent_pi)

        if isinstance(self.action_dist, DiagGaussianDistribution):
            return self.action_dist.proba_distribution(mean_actions, self.log_std)
        elif isinstance(self.action_dist, CategoricalDistribution):
            # Here mean_actions are the logits before the softmax
            return self.action_dist.proba_distribution(action_logits=mean_actions)
        elif isinstance(self.action_dist, MultiCategoricalDistribution):
            # Here mean_actions are the flattened logits
            return self.action_dist.proba_distribution(action_logits=mean_actions)
        elif isinstance(self.action_dist, BernoulliDistribution):
            # Here mean_actions are the logits (before rounding to get the binary actions)
            return self.action_dist.proba_distribution(action_logits=mean_actions)
        elif isinstance(self.action_dist, StateDependentNoiseDistribution):
            return self.action_dist.proba_distribution(mean_actions, self.log_std, latent_sde)
        else:
            raise ValueError("Invalid action distribution")