Rl Policy Optimization
Best practices for reinforcement learning policy optimization.
Install
npx skills add aiming-lab/AutoResearchClaw --skill rl-policy-optimization -g -a claude-code -y- Repository
- aiming-lab/AutoResearchClaw14.8k
- Category
- Design
- Updated
- 3mo ago
- License
- MIT
- Context cost
- Lightabout 206 tokens
- Runs scripts
- No, instructions only
What it does
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
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
