[Question] SAC Lagrangian training not converging properly

[seominseok00]

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Questions

Hello,

I’m currently testing the SAC Lagrangian implementation provided by OmniSafe.

I followed the README instructions by cloning the repository and installing it using pip install -e .. I then applied it to the SafetyPointGoal1 task from Safety Gymnasium.

I used the default configuration:

{
    "seed": 0,
    "train_cfgs": {
        "device": "cpu",
        "torch_threads": 1,
        "vector_env_nums": 1,
        "parallel": 1,
        "total_steps": 10000000,
        "eval_episodes": 1,
        "epochs": 5000
    },
    "algo_cfgs": {
        "steps_per_epoch": 2000,
        "update_cycle": 1,
        "update_iters": 1,
        "size": 1000000,
        "batch_size": 256,
        "reward_normalize": false,
        "cost_normalize": false,
        "obs_normalize": false,
        "max_grad_norm": 40,
        "use_critic_norm": false,
        "critic_norm_coeff": 0.001,
        "polyak": 0.005,
        "gamma": 0.99,
        "start_learning_steps": 10000,
        "policy_delay": 2,
        "use_exploration_noise": false,
        "exploration_noise": 0.1,
        "policy_noise": 0.2,
        "policy_noise_clip": 0.5,
        "alpha": 1e-05,
        "auto_alpha": false,
        "use_cost": true,
        "warmup_epochs": 100
    },
    "logger_cfgs": {
        "use_wandb": false,
        "wandb_project": "omnisafe",
        "use_tensorboard": true,
        "save_model_freq": 100,
        "log_dir": "./runs",
        "window_lens": 10
    },
    "model_cfgs": {
        "weight_initialization_mode": "kaiming_uniform",
        "actor_type": "gaussian_sac",
        "linear_lr_decay": false,
        "actor": {
            "hidden_sizes": [
                256,
                256
            ],
            "activation": "relu",
            "lr": 5e-06
        },
        "critic": {
            "num_critics": 2,
            "hidden_sizes": [
                256,
                256
            ],
            "activation": "relu",
            "lr": 0.001
        }
    },
    "lagrange_cfgs": {
        "cost_limit": 25.0,
        "lagrangian_multiplier_init": 0.0,
        "lambda_lr": 5e-07,
        "lambda_optimizer": "Adam"
    },
    "exp_increment_cfgs": {
        "train_cfgs": {
            "parallel": 1,
            "total_steps": 10000000,
            "device": "cpu",
            "vector_env_nums": 1,
            "torch_threads": 1
        }
    },
    "exp_name": "SACLag-{SafetyPointGoal1-v0}",
    "env_id": "SafetyPointGoal1-v0",
    "algo": "SACLag"
}

However, the learning performance is quite different from the graphs shown on the OmniSafe benchmark page for off-policy algorithms.

Will running multiple seeds and computing the mean/variance help the results match those benchmark curves more closely?

Also, while PPO Lagrangian seems to reduce the cost in a stable manner, SAC Lagrangian does not. Why might that be the case?

[Gaiejj]

I think it might be because the plotter you're using doesn't have smoothing enabled. Please try using TensorBoard's smoothing function or utilize the built-in plotter from Omnisafe for your graphing needs.

[seominseok00]

@Gaiejj Thank you for your response.

What I’m curious about is this: in each epoch, only 2 episodes are actually run, but the window_lens in logger_cfgs is set to 10.

So when plotting the graph, the values from 8 previous episodes are already being reflected.

Doesn’t this mean that smoothing is already being applied?

Even so, would it still be okay to apply additional smoothing on top of that?

[Gaiejj]

Yes, additional smoothing of the plotter should be applied to better show the trend.

[seominseok00]

Sorry to bother you again.

I tried plotting using the plotter you provided, but the trend in the graph doesn’t show up at all.

Is that just because I happened to pick a bad seed?

If I train multiple times and then smooth the results before plotting, will the graph show a similar trend to yours?

or when you ran the training, did the cost decrease stably even with just a single run?

[Gaiejj]

I suspect the reason may be that you ran too many steps (only 3e6 steps were used in our published graphs), causing algorithm instability in later stages. You might try using smaller learning rates for both the actor and Lagrangian multipliers. This configuration has proven highly effective for off-policy algorithms like SAC Lag, as they update at every step and are prone to oscillations.

[seominseok00]

Alright, I’ll give it a try as you suggested and get back to you afterward.

Thank you 😀