| import optuna |
|
|
| from cleanrl_utils.tuner import Tuner |
|
|
| tuner = Tuner( |
| script="cleanrl/ppo.py", |
| metric="charts/episodic_return", |
| metric_last_n_average_window=50, |
| direction="maximize", |
| aggregation_type="average", |
| target_scores={ |
| "CartPole-v1": [0, 500], |
| "Acrobot-v1": [-500, 0], |
| }, |
| params_fn=lambda trial: { |
| "learning-rate": trial.suggest_float("learning-rate", 0.0003, 0.003, log=True), |
| "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), |
| "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4, 8]), |
| "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), |
| "vf-coef": trial.suggest_float("vf-coef", 0, 5), |
| "max-grad-norm": trial.suggest_float("max-grad-norm", 0, 5), |
| "total-timesteps": 100000, |
| "num-envs": 16, |
| }, |
| pruner=optuna.pruners.MedianPruner(n_startup_trials=5), |
| sampler=optuna.samplers.TPESampler(), |
| ) |
| tuner.tune( |
| num_trials=100, |
| num_seeds=3, |
| ) |
|
|
