def _init() -> gym.Env: env = gym.make(env_id) # Create folder if needed if log_dir is not None: os.makedirs(log_dir, exist_ok=True) env = Monitor(env, log_dir) env.seed(seed + rank) return env
def _init(): set_random_seed(seed + rank) env = gym.make(env_id, **env_kwargs) # Wrap first with a monitor (e.g. for Atari env where reward clipping is used) log_file = os.path.join(log_dir, str(rank)) if log_dir is not None else None # Monitor success rate too for the real robot info_keywords = ('is_success', ) if 'NeckEnv' in env_id else () env = Monitor(env, log_file, info_keywords=info_keywords) # Dict observation space is currently not supported. # https://github.com/hill-a/stable-baselines/issues/321 # We allow a Gym env wrapper (a subclass of gym.Wrapper) if wrapper_class: env = wrapper_class(env) env.seed(seed + rank) return env
def _init() -> gym.Env: env = gym.make(env_id) env = Monitor(env, log_dir) env.seed(seed + rank) return env
def _init(): env = gym.make(env_id, reward_type="dense") env = gym.wrappers.FlattenObservation(env) env = Monitor(env) env.seed(seed + rank) return env