ff9c1dac887643e464f5f829c7d8b920b0aa8140,rllib/agents/ddpg/ddpg_torch_policy.py,,ddpg_actor_critic_loss,#Any#Any#Any#Any#,30

Before Change

                std=policy.config["target_noise"]), -target_noise_clip,
            target_noise_clip)
        policy_tp1_smoothed = torch.clamp(policy_tp1 + clipped_normal_sample,
                                          policy.action_space.low.item(0),
                                          policy.action_space.high.item(0))
    else:
        // No smoothing, just use deterministic actions.

After Change

    if policy.config["smooth_target_policy"]:
        target_noise_clip = policy.config["target_noise_clip"]
        clipped_normal_sample = torch.clamp(
            torch.normal(
                mean=torch.zeros(policy_tp1.size()),
                std=policy.config["target_noise"]).to(policy_tp1.device),
            -target_noise_clip,
            target_noise_clip)

        policy_tp1_smoothed = torch.min(
            torch.max(
                policy_tp1 + clipped_normal_sample,
                torch.tensor(
                    policy.action_space.low,
                    dtype=torch.float32,
                    device=policy_tp1.device)),
            torch.tensor(
                policy.action_space.high,
                dtype=torch.float32,
                device=policy_tp1.device))
    else:
        // No smoothing, just use deterministic actions.
        policy_tp1_smoothed = policy_tp1

Instances