PPOStateAdapter
python
PPOStateAdapter(config: PPOExperimentConfiguration, learner_model: torch.nn.Module, learner_model_container: ThreadSafeModel, actor_model: torch.nn.Module, optimizer: torch.optim.Optimizer, trainer: ActorRuntime, learner: LearnerRuntime, tester: TesterRuntime | None, tester_model: torch.nn.Module | None) -> NoneCaptures and restores PPO-specific runtime state.
Initialize the PPO state adapter.
Parameters:
config(PPOExperimentConfiguration) – PPO experiment configuration.learner_model(Module) – Learner model module.learner_model_container(ThreadSafeModel) – Thread-safe learner model wrapper.actor_model(Module) – Actor model module.optimizer(Optimizer) – Optimizer used by the learner.trainer(ActorRuntime) – Trainer runtime object.learner(LearnerRuntime) – Learner runtime object.tester(TesterRuntime | None) – Optional tester runtime object.tester_model(Module | None) – Optional tester model module.
Methods
create_checkpoint_files
python
create_checkpoint_files(*, elapsed_time: float, total_rollbacks: int, run_name: str) -> list[CheckpointFile]Build the PPO-specific checkpoint files for the current runtime state.
Parameters:
elapsed_time(float) – Total elapsed experiment time.total_rollbacks(int) – Total number of performed rollbacks.run_name(str) – The run name to persist.
Returns:
list[CheckpointFile]– The list of checkpoint files to save.
restore_for_rollback
python
restore_for_rollback(checkpoint: Checkpoint) -> intRestore only the learner/optimizer state required for automatic rollback.
Parameters:
checkpoint(Checkpoint) – The checkpoint selected for rollback.
Returns:
int– The actor step recorded inside the restored checkpoint.
restore_full
python
restore_full(checkpoint: Checkpoint) -> PPORestoreMetadataRestore full experiment runtime state from a checkpoint.
Parameters:
checkpoint(Checkpoint) – The checkpoint to restore from.
Returns:
PPORestoreMetadata– Metadata needed by the experiment after restore.