RL Policy Optimization Best Practices
Guides an agent through reinforcement learning algorithm choice, training recipes and fair evaluation.
- Repository
- aiming-lab/AutoResearchClaw
- Stars
- 14,802
- Licence
- MIT
- Files
- 1 file
What it does
This is a instruction-only skill: a checklist of reinforcement learning best practices that an AI agent loads when a task involves RL agents, PPO, SAC or reward design. Once installed, the agent suggests suitable algorithms for discrete, continuous, multi-agent or offline settings, proposes typical starting hyperparameters, and reminds you to evaluate properly (multiple seeds, several evaluation episodes, comparison against baselines). It contains no code of its own and does not change anything on your machine.
Great for
- Engineers starting a reinforcement learning project who want sensible defaults for PPO or SAC
- Researchers writing up RL experiments who need a reminder about seeds, baselines and reporting
- Students learning RL who want a concise overview of algorithm families and common pitfalls
Skip it if
- Production systems that need a tested, verified training pipeline: the skill gives generic guidance, and its hyperparameters are starting points, not guarantees for your environment
- Non-technical users looking for a finished RL application: it only advises, it does not build or run training
Try asking
- “Which algorithm should I use for a continuous-control robot arm task, PPO or SAC?”
- “Review my reward function for a navigation agent and point out possible bias from reward shaping.”
- “Help me design an evaluation protocol for my PPO agent that is fair and reproducible.”
- “What hyperparameters should I start with for SAC and how should I sweep them?”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 1 original files, the MIT licence and an attribution note.
rl-policy-optimization.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip rl-policy-optimization.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip rl-policy-optimization.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Name your environment and action space (discrete or continuous) so the agent can narrow the algorithm choice.
- Treat the listed hyperparameters as starting points and verify them against your own environment and reference papers.
- Ask the agent to plan a multi-seed (5+) evaluation before running experiments, since seed sensitivity is high.
What's inside
- SKILL.md
Redistributed unmodified from aiming-lab/AutoResearchClaw under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.