sinergym.utils.wrappers.LoggerWrapper
- class sinergym.utils.wrappers.LoggerWrapper(*args: Any, **kwargs: Any)
- __init__(env: ~typing.Any, logger_class: ~typing.Callable = <class 'sinergym.utils.logger.CSVLogger'>, monitor_header: ~typing.List[str] | None = None, progress_header: ~typing.List[str] | None = None, flag: bool = True)
CSVLogger to log interactions with environment.
- Parameters:
env (Any) – Original Gym environment.
logger_class (CSVLogger) – CSV Logger class to use to log all information.
monitor_header – Header for monitor.csv in each episode. Default is None (default format).
progress_header – Header for progress.csv in whole simulation. Default is None (default format).
flag (bool, optional) – State of logger (activate or deactivate). Defaults to True.
Methods
__init__
(env[, logger_class, ...])CSVLogger to log interactions with environment.
Activate logger if its flag False.
close
()Recording last episode summary and close env.
Deactivate logger if its flag True.
reset
()Resets the environment.
step
(action)Step the environment.
- activate_logger() None
Activate logger if its flag False.
- close() None
Recording last episode summary and close env.
- deactivate_logger() None
Deactivate logger if its flag True.
- reset() ndarray
Resets the environment. Recording episode summary in logger
- Returns:
First observation given
- Return type:
np.ndarray
- step(action: int | ndarray) Tuple[ndarray, float, bool, Dict[str, Any]]
Step the environment. Logging new information
- Parameters:
action (Union[int, np.ndarray]) – Action executed in step
- Returns:
Tuple with next observation, reward, bool for terminated episode and dict with extra information.
- Return type:
Tuple[np.ndarray, float, bool, Dict[str, Any]]