eta_ctrl.config.config_setup module

class eta_ctrl.config.config_setup.ConfigSetup(*, agent_import: str, environment_import: str, interaction_env_import: str | None = None, vectorizer_import='stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv', policy_import='eta_ctrl.common.NoPolicy', monitor_wrapper=False, norm_wrapper_obs=False, norm_wrapper_reward=False, tensorboard_log=False)

Bases: object

Configuration options as specified in the “setup” section of the configuration file.

agent_import: str

Import description string for the agent class.

agent_class: type[BaseAlgorithm]

Agent class (automatically determined from agent_import).

environment_import: str

Import description string for the environment class.

environment_class: type[BaseEnv]

Imported Environment class (automatically determined from environment_import).

interaction_env_import: str | None

Import description string for the interaction environment (default: None).

interaction_env_class: type[BaseEnv] | None

Interaction environment class (default: None) (automatically determined from interaction_env_import).

vectorizer_import: str

Import description string for the environment vectorizer (default: stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv).

vectorizer_class: type[DummyVecEnv]

Environment vectorizer class (automatically determined from vectorizer_import).

policy_import: str

Import description string for the policy class (default: eta_ctrl.agents.common.NoPolicy).

policy_class: type[BasePolicy]

Policy class (automatically determined from policy_import).

monitor_wrapper: bool

Flag which is true if the environment should be wrapped for monitoring (default: False).

norm_wrapper_obs: bool

Flag which is true if the observations should be normalized (default: False).

norm_wrapper_reward: bool

Flag which is true if the rewards should be normalized (default: False).

tensorboard_log: bool

Flag to enable tensorboard logging (default: False).

classmethod from_dict(dikt: dict[str, Any]) → ConfigSetup