The Arguments class contains all parameters of the training process, including environment setup, model training, model evaluation, and resource allocation. It provides users an unified interface to customize the training process.

The class should be initialized at the start of the training process. For example,

from elegantrl.train.config import Arguments
from elegantrl.agents.AgentPPO import AgentPPO
from elegantrl.train.config import build_env
import gym

args = Arguments(build_env('Pendulum-v1'), AgentPPO())

The full list of parameters in Arguments:

Environment registration

elegantrl.train.config.build_env(env_class=None, env_args: Optional[dict] = None, gpu_id: int = -1)[source]


elegantrl.train.config.kwargs_filter(function, kwargs: dict) dict[source]