ray.rllib.algorithms.algorithm_config.AlgorithmConfig.checkpointing#

AlgorithmConfig.checkpointing(export_native_model_files: bool | None = <ray.rllib.utils.from_config._NotProvided object>, checkpoint_trainable_policies_only: bool | None = <ray.rllib.utils.from_config._NotProvided object>) → AlgorithmConfig[source]#

Sets the config’s checkpointing settings.

Parameters:

  • export_native_model_files – Whether an individual Policy- or the Algorithm’s checkpoints also contain (tf or torch) native model files. These could be used to restore just the NN models from these files w/o requiring RLlib. These files are generated by calling the tf- or torch- built-in saving utility methods on the actual models.

  • checkpoint_trainable_policies_only – Whether to only add Policies to the Algorithm checkpoint (in sub-directory “policies/”) that are trainable according to the is_trainable_policy callable of the local worker.

Returns:

This updated AlgorithmConfig object.