rl-policy-optimization

Best practices for reinforcement learning policy optimization.

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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

Use cases: design

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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What's inside

Skill name: rl-policy-optimization
Description: Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
License: MIT
Metadata: author: researchclaw · version: 1.0 · category: domain · priority: 3 · references: Schulman et al., Proximal Policy Optimization, 2017; Haarnoja et al., Soft Actor-Critic, ICML 2018 · trigger-keywords: reinforcement learning,rl,policy,reward,agent,environment,ppo,sac · applicable-stages: 9,10
Instructions: 26 lines in SKILL.md · view on GitHub

Files (1)

SKILL.md

SKILL.md (first lines)

## 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

From raja21068/AutoScholar, MIT.

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Last commit (this folder): Apr 12, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version: 1.0
License: MIT
Repository: github.com/raja21068/AutoScholar
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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rl-policy-optimization skill. Agent Mart. https://agentmart.dev/skills/raja21068-rl-policy-optimization (last updated 2026-10-24)