PPO playing HalfCheetahBulletEnv-v0 from https://github.com/sgoodfriend/rl-algo-impls/tree/2067e21d62fff5db60168687e7d9e89019a8bfc0
6f3bdf9
| import optuna | |
| from typing import Any, Dict | |
| from rl_algo_impls.wrappers.vectorable_wrapper import VecEnv, single_observation_space | |
| def sample_env_hyperparams( | |
| trial: optuna.Trial, env_hparams: Dict[str, Any], env: VecEnv | |
| ) -> Dict[str, Any]: | |
| obs_space = single_observation_space(env) | |
| n_envs = 2 ** trial.suggest_int("n_envs_exp", 1, 5) | |
| trial.set_user_attr("n_envs", n_envs) | |
| env_hparams["n_envs"] = n_envs | |
| normalize = trial.suggest_categorical("normalize", [False, True]) | |
| env_hparams["normalize"] = normalize | |
| if normalize: | |
| normalize_kwargs = env_hparams.get("normalize_kwargs", {}) | |
| if len(obs_space.shape) == 3: | |
| normalize_kwargs.update( | |
| { | |
| "norm_obs": False, | |
| "norm_reward": True, | |
| } | |
| ) | |
| else: | |
| norm_obs = trial.suggest_categorical("norm_obs", [True, False]) | |
| norm_reward = trial.suggest_categorical("norm_reward", [True, False]) | |
| normalize_kwargs.update( | |
| { | |
| "norm_obs": norm_obs, | |
| "norm_reward": norm_reward, | |
| } | |
| ) | |
| env_hparams["normalize_kwargs"] = normalize_kwargs | |
| elif "normalize_kwargs" in env_hparams: | |
| del env_hparams["normalize_kwargs"] | |
| return env_hparams | |