Experiment Manager

Parameters

class rl_zoo3.exp_manager.ExperimentManager(args, algo, env_id, log_folder, tensorboard_log='', n_timesteps=0, eval_freq=10000, n_eval_episodes=5, save_freq=-1, hyperparams=None, env_kwargs=None, trained_agent='', optimize_hyperparameters=False, storage=None, study_name=None, n_trials=1, max_total_trials=None, n_jobs=1, sampler='tpe', pruner='median', optimization_log_path=None, n_startup_trials=0, n_evaluations=1, truncate_last_trajectory=False, uuid_str='', seed=0, log_interval=0, save_replay_buffer=False, verbose=1, vec_env_type='dummy', n_eval_envs=1, no_optim_plots=False, device='auto', config=None, show_progress=False)

Experiment manager: read the hyperparameters, preprocess them, create the environment and the RL model.

Please take a look at train.py to have the details for each argument.

create_envs(n_envs, eval_env=False, no_log=False)

Create the environment and wrap it if necessary.

Parameters:

• n_envs (int) –

• eval_env (bool) – Whether is it an environment used for evaluation or not

• no_log (bool) – Do not log training when doing hyperparameter optim (issue with writing the same file)

Return type:

VecEnv

Returns:

the vectorized environment, with appropriate wrappers

learn(model)

Parameters:

model (BaseAlgorithm) – an initialized RL model

Return type:

None

save_trained_model(model)

Save trained model optionally with its replay buffer and VecNormalize statistics

Parameters:

model (BaseAlgorithm) –

Return type:

None

setup_experiment()

Read hyperparameters, pre-process them (create schedules, wrappers, callbacks, action noise objects) create the environment and possibly the model.

Return type:

Optional[Tuple[BaseAlgorithm, Dict[str, Any]]]

Returns:

the initialized RL model