Example on how to use Optuna for automatic hyperparamer optimization with RL and SB3

optimize_ppo.py

"""Optuna example that optimizes the hyperparameters of
a reinforcement learning agent using PPO implementation from Stable-Baselines3
on a Gymnasium environment.

This is a simplified version of what can be found in https://github.com/DLR-RM/rl-baselines3-zoo.

You can run this example as follows:
    $ python optimize_ppo.py

"""

from typing import Any

import gymnasium
import optuna
from optuna.pruners import MedianPruner
from optuna.samplers import TPESampler
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.env_util import make_vec_env
import torch
import torch.nn as nn


N_TRIALS = 500
N_STARTUP_TRIALS = 10
N_EVALUATIONS = 2
N_TIMESTEPS = 40_000
EVAL_FREQ = int(N_TIMESTEPS / N_EVALUATIONS)
N_EVAL_EPISODES = 10

ENV_ID = "Pendulum-v1"
N_ENVS = 5

DEFAULT_HYPERPARAMS = {
    "policy": "MlpPolicy",
}


def sample_ppo_params(trial: optuna.Trial) -> dict[str, Any]:
    """Sampler for PPO hyperparameters."""
    # From 2**5=32 to 2**12=4096
    n_steps_pow = trial.suggest_int("n_steps_pow", 5, 12)
    gamma = trial.suggest_float("gamma", 0.97, 0.9999)
    learning_rate = trial.suggest_float("learning_rate", 3e-5, 3e-3, log=True)
    activation_fn = trial.suggest_categorical("activation_fn", ["tanh", "relu"])

    n_steps = 2**n_steps_pow
    # Display true values
    trial.set_user_attr("n_steps", n_steps)
    # Convert to PyTorch objects
    activation_fn = {"tanh": nn.Tanh, "relu": nn.ReLU}[activation_fn]

    return {
        "n_steps": n_steps,
        "gamma": gamma,
        "learning_rate": learning_rate,
        "policy_kwargs": {
            "activation_fn": activation_fn,
        },
    }


class TrialEvalCallback(EvalCallback):
    """Callback used for evaluating and reporting a trial."""

    def __init__(
        self,
        eval_env: gymnasium.Env,
        trial: optuna.Trial,
        n_eval_episodes: int = 5,
        eval_freq: int = 10000,
        deterministic: bool = True,
        verbose: int = 0,
    ):
        super().__init__(
            eval_env=eval_env,
            n_eval_episodes=n_eval_episodes,
            eval_freq=eval_freq,
            deterministic=deterministic,
            verbose=verbose,
        )
        self.trial = trial
        self.eval_idx = 0
        self.is_pruned = False

    def _on_step(self) -> bool:
        if self.eval_freq > 0 and self.n_calls % self.eval_freq == 0:
            super()._on_step()
            self.eval_idx += 1
            self.trial.report(self.last_mean_reward, self.eval_idx)
            # Prune trial if need.
            if self.trial.should_prune():
                self.is_pruned = True
                return False
        return True


def objective(trial: optuna.Trial) -> float:
    vec_env = make_vec_env(ENV_ID, n_envs=N_ENVS)
    kwargs = DEFAULT_HYPERPARAMS.copy()
    # Sample hyperparameters.
    kwargs.update(sample_ppo_params(trial))
    # Create the RL model.
    model = PPO(env=vec_env, **kwargs)
    # Create env used for evaluation.
    eval_env = make_vec_env(ENV_ID, n_envs=N_ENVS)
    # Create the callback that will periodically evaluate and report the performance.
    eval_callback = TrialEvalCallback(
        eval_env,
        trial,
        n_eval_episodes=N_EVAL_EPISODES,
        eval_freq=max(EVAL_FREQ // N_ENVS, 1),
        deterministic=True,
    )

    nan_encountered = False
    try:
        model.learn(N_TIMESTEPS, callback=eval_callback)
    except AssertionError as e:
        # Sometimes, random hyperparams can generate NaN.
        print(e)
        nan_encountered = True
    finally:
        # Free memory.
        model.env.close()
        eval_env.close()

    # Tell the optimizer that the trial failed.
    if nan_encountered:
        return float("nan")

    if eval_callback.is_pruned:
        raise optuna.exceptions.TrialPruned()

    return eval_callback.last_mean_reward


if __name__ == "__main__":
    # Set pytorch num threads to 1 for faster training.
    torch.set_num_threads(1)

    sampler = TPESampler(n_startup_trials=N_STARTUP_TRIALS, multivariate=True)
    # Do not prune before 1/3 of the max budget is used.
    pruner = MedianPruner(
        n_startup_trials=N_STARTUP_TRIALS, n_warmup_steps=N_EVALUATIONS // 3
    )

    study = optuna.create_study(sampler=sampler, pruner=pruner, direction="maximize")
    try:
        study.optimize(objective, n_trials=N_TRIALS, timeout=600)
    except KeyboardInterrupt:
        pass

    print(f"Number of finished trials: {len(study.trials)}")

    print("Best trial:")
    trial = study.best_trial

    print("  Value: ", trial.value)

    print("  Params: ")
    for key, value in trial.params.items():
        print(f"    {key}: {value}")

    print("  User attrs:")
    for key, value in trial.user_attrs.items():
        print(f"    {key}: {value}")