Configs

GeneLab configuration is a dataclass tree. The tree is intentionally ordinary Python rather than a custom schema language: task authors can construct it with normal code, and the CLI can still expose stable override paths.

The core idea

TaskCfg is the boundary between discovery and runtime. It records the task id, the registered env and robot names, the default env config, an optional play-mode env config, and an optional agent config.

TaskCfg
├── env
├── play_env
└── agent

env is typed as object on purpose. Downstream projects can use a subclass such as ManagerBasedRlEnvCfg without changing GeneLab core.

Why dotted overrides work

The CLI treats --a.b.c VALUE as a path through this dataclass tree. apply_overrides resolves the path, reads the current value and annotation, coerces the string value, and mutates the config before runtime starts.

That keeps experiments reproducible: the final TaskCfg can be dumped, and invalid paths fail early instead of silently being ignored.

Play configs are first-class

Training and inspection often need different defaults. play_env lets a task keep viewer-friendly settings next to training settings: fewer envs, viewer on, optional mouse interaction, and optional recording outputs.

Where to continue

• Configuration Reference
• Design a Task
• CLI Reference