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name: agilerl description: "Use AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AgileRL

Use this repo skill when a task involves AgileRL, agilerl, evolutionary hyperparameter optimization for RL, Gymnasium/PettingZoo training with AgileRL algorithms, AgileRL evolvable modules/networks, offline RL datasets, contextual bandits, or AgileRL LLM post-training.

Quick Start

• Install: pip install agilerl.
• Optional LLM stack: pip install "agilerl[llm]" only when the task uses GRPO/CISPO/GSPO, DPO/SFT, vLLM, PEFT, DeepSpeed, or transformer datasets.
• Import check: python -c "import agilerl, importlib.metadata as md; print(md.version('agilerl'))".
• Read references/package-overview.md for the algorithm family map, references/troubleshooting.md for cross-cutting dependency/backend issues, and references/repo-provenance.md when checking whether this skill is stale.
• Run scripts/check_agilerl_install.py --help for a safe local capability probe; it does not train, download models, or require the source checkout.

Route By Task

User task	Read
Train PPO, DQN, RainbowDQN, DDPG, or TD3 on Gymnasium; wire create_population, train_on_policy, train_off_policy, replay/rollout buffers, or distributed classical training	sub-skills/training-workflows/SKILL.md
Configure tournament selection, mutations, mutable RL hyperparameters, architecture mutation probabilities, or population evolution	sub-skills/hpo-and-mutation/SKILL.md
Build custom EvolvableModule/EvolvableNetwork objects, net_config, MLP/CNN/LSTM/MultiInput/SimBa configs, actors, critics, or DummyEvolvable wrappers	sub-skills/evolvable-modules/SKILL.md
Use PettingZoo multi-agent training, MADDPG, MATD3, IPPO, AsyncPettingZooVecEnv, agent id grouping, or AgileRL wrappers	sub-skills/multi-agent-and-wrappers/SKILL.md
Use offline datasets, HDF5/Minari conversion, replay filling, CQL/ILQL, contextual bandits, NeuralUCB, NeuralTS, or BanditEnv	sub-skills/offline-bandits-data/SKILL.md
Plan LLM fine-tuning/post-training with GRPO, CISPO, GSPO, LLM PPO, REINFORCE, SFT, DPO, vLLM, DeepSpeed, PEFT, or AgileRL LLM envs	sub-skills/llm-fine-tuning/SKILL.md
Compare benchmark scripts or adapt performance runs safely	references/benchmarking.md

Common AgileRL Building Blocks

• Algorithms: PPO, DQN, RainbowDQN, DDPG, TD3, CQN, ILQL, NeuralUCB, NeuralTS, MADDPG, MATD3, IPPO, GRPO, CISPO, GSPO, LLM PPO, REINFORCE, SFT, and DPO.
• Population setup usually starts with agilerl.utils.utils.create_population(...) and a Gymnasium or PettingZoo observation/action space.
• Evolution usually combines TournamentSelection(...) and Mutations(...); use HyperparameterConfig and RLParameter for mutable algorithm attributes.
• Network configuration usually uses net_config with encoder_config, head_config, and optional recurrent/CNN/MultiInput fields.
• Training helpers return trained populations and fitness metrics; full training can be long, so prefer import/config/smoke checks before launching runs.

Safe Validation Pattern

1. Check installation and optional dependency availability with scripts/check_agilerl_install.py.
2. Validate spaces, algorithm names, INIT_HP, net_config, and HPO configs with the nearest sub-skill helper script.
3. Run tiny constructor/import checks before full training.
4. Treat demos, tutorials, benchmark launchers, and original repo tests as evidence only; do not depend on the AgileRL source checkout from this generated skill.
5. For long training, LLM, GPU, vLLM, DeepSpeed, Minari remote, UCI, or benchmark tasks, document required hardware/data/network first and ask before expensive execution.

Troubleshooting First Reads

• Install/import, Python version, missing optional extras, Torch/Gymnasium/PettingZoo/JAX/W&B issues: references/troubleshooting.md.
• Training shape/config errors: sub-skills/training-workflows/references/troubleshooting.md.
• Mutation/HPO mistakes: sub-skills/hpo-and-mutation/references/troubleshooting.md.
• Network architecture/config mistakes: sub-skills/evolvable-modules/references/troubleshooting.md.
• Multi-agent env/wrapper mistakes: sub-skills/multi-agent-and-wrappers/references/troubleshooting.md.
• Offline/bandit data schema mistakes: sub-skills/offline-bandits-data/references/troubleshooting.md.
• LLM optional dependency and backend mistakes: sub-skills/llm-fine-tuning/references/troubleshooting.md.