AI agent skill · by @aiming-lab

Rl Policy Optimization

Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.

Archived 2026-10-23 · Source updated 2026-08-19 · License: MIT. Check the original repository for the latest version.

SKILL.md instructions

## RL Policy Optimization Best Practice
Algorithm selection:
- Discrete actions: PPO, DQN, A2C
- Continuous actions: SAC, TD3, PPO
- Multi-agent: MAPPO, QMIX
- Offline: CQL, IQL, Decision Transformer

Training recipe:
- PPO: clip=0.2, lr=3e-4, gamma=0.99, GAE lambda=0.95
- SAC: lr=3e-4, tau=0.005, auto-tune alpha
- Use vectorized environments (e.g., gymnasium.vector)
- Normalize observations and rewards
- Log episode return, episode length, value loss, policy entropy

Evaluation:
- Report mean +/- std over 10+ evaluation episodes
- Use deterministic policy for evaluation
- Compare against random policy and simple baselines
- Report sample efficiency (return vs. env steps)

Common pitfalls:
- Reward shaping can introduce bias
- Seed sensitivity is HIGH — use 5+ seeds
- Hyperparameter sensitivity — do a small sweep