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, eval_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)[source]
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.
- Parameters:
args (Namespace) –
algo (str) –
env_id (str) –
log_folder (str) –
tensorboard_log (str) –
n_timesteps (int) –
eval_freq (int) –
n_eval_episodes (int) –
save_freq (int) –
hyperparams (Dict[str, Any] | None) –
env_kwargs (Dict[str, Any] | None) –
eval_env_kwargs (Dict[str, Any] | None) –
trained_agent (str) –
optimize_hyperparameters (bool) –
storage (str | None) –
study_name (str | None) –
n_trials (int) –
max_total_trials (int | None) –
n_jobs (int) –
sampler (str) –
pruner (str) –
optimization_log_path (str | None) –
n_startup_trials (int) –
n_evaluations (int) –
truncate_last_trajectory (bool) –
uuid_str (str) –
seed (int) –
log_interval (int) –
save_replay_buffer (bool) –
verbose (int) –
vec_env_type (str) –
n_eval_envs (int) –
no_optim_plots (bool) –
device (device | str) –
config (str | None) –
show_progress (bool) –
- create_envs(n_envs, eval_env=False, no_log=False)[source]
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)
- Returns:
the vectorized environment, with appropriate wrappers
- Return type:
VecEnv