metarl.np package

Reinforcement Learning Algorithms which use NumPy as a numerical backend.

paths_to_tensors(paths, max_path_length, baseline_predictions, discount)[source]

Return processed sample data based on the collected paths.

Parameters:
  • paths (list[dict]) – A list of collected paths.
  • max_path_length (int) – Maximum length of a single rollout.
  • baseline_predictions (numpy.ndarray) – : Predicted value of GAE (Generalized Advantage Estimation) Baseline.
  • discount (float) – Environment reward discount.
Returns:

Processed sample data, with key
  • observations (numpy.ndarray): Padded array of the observations of
    the environment
  • actions (numpy.ndarray): Padded array of the actions fed to the
    the environment
  • rewards (numpy.ndarray): Padded array of the acquired rewards
  • agent_infos (dict): a dictionary of {stacked tensors or
    dictionary of stacked tensors}
  • env_infos (dict): a dictionary of {stacked tensors or
    dictionary of stacked tensors}
  • rewards (numpy.ndarray): Padded array of the validity information
Return type:

dict