Callbacks

class rl_zoo3.callbacks.ParallelTrainCallback(gradient_steps=100, verbose=0, sleep_time=0.0)

Callback to explore (collect experience) and train (do gradient steps) at the same time using two separate threads. Normally used with off-policy algorithms and train_freq=(1, “episode”).

TODO: - blocking mode: wait for the model to finish updating the policy before collecting new experience at the end of a rollout - force sync mode: stop training to update to the latest policy for collecting new experience

Parameters:

• gradient_steps (int) – Number of gradient steps to do before sending the new policy

• verbose (int) – Verbosity level

• sleep_time (float) – Limit the fps in the thread collecting experience.

class rl_zoo3.callbacks.RawStatisticsCallback(verbose=0)

Callback used for logging raw episode data (return and episode length).

class rl_zoo3.callbacks.SaveVecNormalizeCallback(save_freq, save_path, name_prefix=None, verbose=0)

Callback for saving a VecNormalize wrapper every save_freq steps

Parameters:

• save_freq (int) – (int)

• save_path (str) – (str) Path to the folder where VecNormalize will be saved, as vecnormalize.pkl

• name_prefix (str | None) – (str) Common prefix to the saved VecNormalize, if None (default) only one file will be kept.

• verbose (int)

class rl_zoo3.callbacks.TrialEvalCallback(eval_env, trial, n_eval_episodes=5, eval_freq=10000, deterministic=True, verbose=0, best_model_save_path=None, log_path=None)

Callback used for evaluating and reporting a trial.

Parameters:

• eval_env (VecEnv)

• trial (Trial)

• n_eval_episodes (int)

• eval_freq (int)

• deterministic (bool)

• verbose (int)

• best_model_save_path (str | None)

• log_path (str | None)