---

name: rl-baselines3-zoo description: "Operate RL Baselines3 Zoo package and CLI workflows for Stable-Baselines3 reinforcement-learning experiments." disable-model-invocation: true metadata: disco-role: operating license: MIT

RL Baselines3 Zoo

Use this repo skill when the task involves RL Baselines3 Zoo / rl_zoo3: training Stable-Baselines3 or SB3-Contrib agents from Zoo hyperparameter files, evaluating saved agents, tuning with Optuna, using Gymnasium env IDs and wrappers/callbacks, plotting Zoo logs, or managing Hub/W&B/video integrations.

Start here

1. Confirm the package and command surface with install and environment guidance.
2. Use CLI command map to choose the correct command family.
3. If an error occurs before a workflow is clear, use cross-cutting troubleshooting.
4. If working against a checkout or a newer package version, compare against repository provenance.
5. Use router metadata only for managed repo-skill routing context.

Minimal package checks

python - <<'PY'
import rl_zoo3
print(rl_zoo3.__version__)
print(sorted(rl_zoo3.ALGOS))
PY

python -m rl_zoo3.train --help
python -m rl_zoo3.enjoy --help

For a bundled, non-mutating check:

python scripts/check_rl_zoo3_install.py --check-plots

Route by task

User task	Read
Build, run, resume, or debug a training command; choose --algo, --env, --log-folder, eval/checkpoint/replay flags, device, --gym-packages, or W&B route flags	training-cli
Read, write, or validate RL Zoo YAML/Python hyperparameter configs, default/atari entries, normalize, wrappers/callback config, --conf-file, or --hyperparams quoting	config-hyperparams
Run or diagnose Optuna HPO: -optimize, samplers/pruners, storage/study reuse, --trial-id, distributed workers, HPO report artifacts	tuning-optimization
Load/evaluate an existing local model with enjoy, choose final/best/checkpoint/latest, inspect log/model folders, use no-render evaluation, or plan local benchmark smoke checks	evaluation-and-artifacts
Register custom Gymnasium envs, validate wrapper/callback/policy import strings, use built-in wrappers/callbacks, patch custom algorithms/SBX	custom-components
Plan Hugging Face Hub download/upload/model-card commands, W&B tracking, record_video, record_training, display/ffmpeg/gif requirements	integrations-hub-tracking
Plot monitor/evaluation curves, build all_plots / plot_from_file commands, run bounded benchmark output flows, troubleshoot rliable/plot deps	plotting-benchmarking

Command stance

• Prefer installed-package module commands for train/evaluate:

  python -m rl_zoo3.train --algo ppo --env CartPole-v1 --n-timesteps 1000
  python -m rl_zoo3.enjoy --algo ppo --env CartPole-v1 -f logs --exp-id 0 --no-render
  

• Use rl_zoo3 train / rl_zoo3 enjoy only when the console router imports cleanly. The console router imports plotting modules, so base installs can fail unless plot dependencies are installed.
• Use helper scripts in this skill to build commands, check imports, inspect local layouts, or validate config/component shapes. They do not train, upload, download, render, or call live services unless the helper explicitly says it only reads local files.

Bundled root scripts

• scripts/check_rl_zoo3_install.py: import/version/optional plotting/CUDA probe; safe and non-mutating.
• scripts/build_rl_zoo3_command.py: quick command router that prints an installed-package command and the owning sub-skill. Use sub-skill helpers for deeper validation.

Example:

python scripts/build_rl_zoo3_command.py train -- --algo ppo --env CartPole-v1 --n-timesteps 1000

Optional dependencies and backends

• CPU is enough for CartPole/Pendulum smoke workflows, static config validation, command construction, local artifact inspection, and most troubleshooting.
• Optional simulator families (Atari, MuJoCo, Box2D, PyBullet, highway, Minigrid, robotics, custom envs) require their own packages and sometimes data/ROMs.
• CUDA or another accelerator is optional unless the user specifically asks for accelerator training. Validate PyTorch device availability before using --device cuda in a long run.
• Hub, W&B, video, and plot workflows may require network, credentials, display/offscreen rendering, ffmpeg, or plot extras; route to the owning sub-skill first.

Do not use this skill for

• Raw Stable-Baselines3 algorithm API questions that do not involve RL Zoo commands, hyperparams, artifact layout, or scripts.
• Generic Gymnasium environment authoring unless the env must be registered/used through RL Zoo.
• LLM RLHF/post-training workflows unrelated to Gymnasium/SB3 agents.