# Rl Policy Optimization

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

- Skill: `enuno/rl-policy-optimization` (Agent Skill)
- Install (CLI): `npx skillmds@latest add enuno/rl-policy-optimization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/enuno/rl-policy-optimization/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: enuno (https://skillmd.com/u/enuno)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/enuno/rl-policy-optimization

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## 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