| name | rl-isaaclab |
| description | Entry point for Moleworks IsaacLab RL work in `moleworks_ext`. Use when working on local smoke tests, Euler submits, run sync, policy playback, benchmark interpretation, or experiment tracking. Routes to narrower IsaacLab skills for cluster ops and benchmarking. |
RL + IsaacLab Router
Use this as the default entry point for moleworks_ext RL work. Keep it thin and load narrower skills for the specific workflow step you need.
Repo Root
- Prefer the active
moleworks_ext checkout the user is already using.
- If you need a default on this machine, start from
/home/lorenzo/moleworks/moleworks_ext.
- Read repo
AGENTS.md plus docs/AI_RESEARCHER_WORKFLOW.md before changing workflow or commands.
Always-Loaded Rules
- Any script that touches Isaac Sim or IsaacLab must run via
/workspace/isaaclab/isaaclab.sh -p.
- Local smoke first, with W&B disabled.
- Real training runs use W&B and should be tracked in the experiment docs.
- Do not recommend checkpoints from sync alone. Require benchmark evidence.
- Prefer repo scripts and checked-in helpers over ad-hoc command reconstruction.
- Do not run long noisy training jobs yourself unless the user explicitly wants live execution and the output is tightly bounded.
Route To Narrower Skills
- For cluster submit,
squeue/sacct, failed-job debugging, sync, and docs/EXPERIMENTS_* hygiene, also use rl-isaaclab-cluster-ops.
- For checkpoint benchmarking, policy playback, TensorBoard temporal plots, and run-to-run comparisons, also use
rl-isaaclab-benchmark.
- If IsaacLab debugging also involves ROS parity stacks, TF, Dig3D replay, or controller-side checks, also use
newton-ros-parity and ros2-debugging.
- If the task needs many code/doc searches, W&B queries, log inspection, or benchmark diffs, also use
moleworks-subagent-orchestrator.
Branch References
Read only the docs that match the current task:
docs/AI_RESEARCHER_WORKFLOW.md
docs/EXPERIMENTS_ONGOING.md
docs/EXPERIMENTS_RUN.md
docs/MULTI_GPU_TRAINING.md
docs/MULTI_GPU_BALANCED_TRAINING.md
Default Starting Points
- Train:
scripts/rsl_rl/train.py
- Play:
scripts/rsl_rl/play.py
- Cluster submit:
docker/cluster/cluster_interface.sh
- Cluster sync:
docker/cluster/sync_experiments.sh
- Live run report:
scripts/utils/cluster_run_report.sh
- Config diff:
scripts/utils/compare_run_configs.py
- Excavation benchmark:
scripts/mole_environments/excavation3D/benchmark_excavation.py
- Temporal plots:
scripts/plot_tb_scalars.py
If the task starts to sprawl across cluster ops, benchmarks, ROS parity, and repo archaeology at the same time, stop and load the narrower skills rather than expanding this one.