schola.scripts.ray.settings.RLlibScriptArgs

class schola.scripts.ray.settings.RLlibScriptArgs(enable_checkpoints=False, checkpoint_dir=’./ckpt’, save_freq=100000, name_prefix_override=None, export_onnx=False, save_final_policy=False, launch_unreal=False, port=None, executable_path=None, headless=False, map=None, fps=None, disable_script=False, training_settings=<factory>, algorithm_settings=<factory>, logging_settings=<factory>, resume_settings=<factory>, network_architecture_settings=<factory>, resource_settings=<factory>, plugins=<factory>)[source]

Bases: ScriptArgs

Top level dataclass for RLlib script arguments. This class aggregates all the settings required for configuring the RLlib training process. It includes settings for training, algorithms, logging, resuming from checkpoints, network architecture, and resource allocation. This allows for a comprehensive configuration of the RLlib training job in a structured manner.

Methods

__init__([enable_checkpoints, …])

make_unreal_connection()

Create an Unreal Engine connection based on the script arguments.

Attributes

checkpoint_dir

Enable saving checkpoints.

disable_script

Flag indicating if the autolaunch script setting in the Unreal Engine Schola Plugin should be disabled.

enable_checkpoints

Enable saving checkpoints

executable_path

Path to the standalone executable, when launching a standalone Environment

export_onnx

Whether to export the model to ONNX format instead of just saving a checkpoint.

fps

Fixed FPS to use when running standalone, if None no fixed timestep is used

headless

Flag indicating if the standalone Unreal Engine process should run in headless mode

launch_unreal

Flag indicating if the script should launch a standalone Unreal Engine process

map

Map to load when launching a standalone Unreal Engine process

name_prefix_override

Override the name prefix for the checkpoint files (e.g. SAC, PPO, etc.).

port

Port to connect to the Unreal Engine process, if None an open port will be automatically selected when running standalone.

save_final_policy

Whether to save the final policy after training is complete.

save_freq

Frequency with which to save checkpoints.

training_settings

algorithm_settings

logging_settings

resume_settings

network_architecture_settings

resource_settings

plugins

Parameters:
__init__(enable_checkpoints=False, checkpoint_dir=’./ckpt’, save_freq=100000, name_prefix_override=None, export_onnx=False, save_final_policy=False, launch_unreal=False, port=None, executable_path=None, headless=False, map=None, fps=None, disable_script=False, training_settings=<factory>, algorithm_settings=<factory>, logging_settings=<factory>, resume_settings=<factory>, network_architecture_settings=<factory>, resource_settings=<factory>, plugins=<factory>)
Parameters:
Return type:

None

algorithm_settings: PPOSettings | APPOSettings | IMPALASettings
logging_settings: LoggingSettings
network_architecture_settings: NetworkArchitectureSettings
plugins: List[RLLibLauncherExtension]
resource_settings: ResourceSettings
resume_settings: ResumeSettings
training_settings: TrainingSettings

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  • Visit the Schola product page for download links and more information.

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