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name: rl-training description: "Use rl-training for Isaac Lab unified train/play entrypoints, RL library selection, checkpoint playback, video capture, and typed preset selectors in RL workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Reinforcement Learning Training

Use this sub-skill when the task is about training, evaluating, or replaying agents with Isaac Lab's RL wrappers.

Route here for

• Running the unified train and play entrypoints.
• Choosing an RL backend library: rl_games, rsl_rl, sb3, skrl, or the contributed rlinf path when relevant.
• Loading or locating checkpoints, last-run folders, and best-model files.
• Recording videos during evaluation runs.
• Using typed preset selectors in RL commands.
• Diagnosing wrapper, checkpoint, or dependency errors that happen before the environment launches.

Use other subskills for

• Selector grammar, environment listing, and task discovery: ../tasks-and-presets/SKILL.md.
• Simulation backend choice or camera/runtime startup: ../simulation-core/SKILL.md.
• Data collection, teleoperation, or imitation learning: ../imitation-and-teleop/SKILL.md.

Working references

• references/rl-library-matrix.md describes the supported libraries and install extras.
• references/train-and-play-workflows.md describes the command flow, common arguments, and checkpoint patterns.
• references/troubleshooting.md covers dispatch and run-time failures.
• scripts/inspect_rl_dispatch.py prints safe command skeletons and library metadata.

Acceptance checks

• Name the correct --rl_library value and wrapper command for the requested workflow.
• Preserve typed preset tokens until setup_preset_cli and Hydra consume them.
• Identify the expected checkpoint path or run-directory convention for the selected library.
• Call out when the requested library or export path needs optional dependencies that are not part of the core install.