Guide · Agent skill
How to use the RL Policy Optimization skill for AI agents
Updated · written with AI from the project's own documentation and checked against our catalogue data
The rl-policy-optimization skill is a set of reinforcement learning best practices that an AI agent loads when your task involves RL agents, PPO, SAC or reward design. It is written for engineers starting an RL project, researchers writing up experiments, and students who want a concise overview of algorithm families and common pitfalls. After reading this guide you will know how to install the skill, how to trigger it with a natural request, and what it will and will not do for you.
RL Policy Optimization Best Practicesaiming-lab/AutoResearchClaw · MIT Download the skill Quick start
- 1Download the ZIP. Download the skill ZIP from this page.
- 2Unzip into a skills folder. Unzip it into ~/.claude/skills/ for personal use, or into .claude/skills/ inside your project.
- 3Or upload in the Claude apps. In the Claude apps, go to Settings → Capabilities → Skills and upload the ZIP.
- 4Describe your RL task. Ask in plain language about your RL agent, PPO, SAC or reward design problem.
- 5Review the suggestions. Check the algorithm choice and starting hyperparameters against your own environment.
- 6Test before trusting. Run multiple seeds and a small sweep, then compare against baselines.
What does the skill make the agent do?
This is an instruction-only skill. It is a checklist that the agent reads when a task matches its description. It contains no code of its own and does not change anything on your machine.
Once loaded, the agent can suggest algorithms for discrete, continuous, multi-agent or offline settings, propose typical starting hyperparameters, and remind you to evaluate properly.
- Algorithm suggestions: PPO, DQN and A2C for discrete actions; SAC, TD3 and PPO for continuous actions; MAPPO and QMIX for multi-agent; CQL, IQL and Decision Transformer for offline settings.
- Starting hyperparameters: for example PPO with clip 0.2, learning rate 3e-4, gamma 0.99 and GAE lambda 0.95; for SAC, learning rate 3e-4, tau 0.005 and automatic alpha tuning.
- Training habits: use vectorized environments, normalize observations and rewards, and log episode return, episode length, value loss and policy entropy.
- Evaluation reminders: report mean and standard deviation over 10 or more evaluation episodes, use a deterministic policy, compare against a random policy and simple baselines, and report sample efficiency.
- Pitfalls: reward shaping can introduce bias, seed sensitivity is high so use 5 or more seeds, and hyperparameters are sensitive so run a small sweep.
How do you install the skill?
Download the ZIP file from this page. Then choose one of two routes depending on how you use your agent.
For a personal setup, unzip the file into your skills folder at ~/.claude/skills/. For a single project, unzip it into the .claude/skills/ folder inside that project. The skill's main file is SKILL.md.
If you use the Claude apps instead, upload the ZIP under Settings → Capabilities → Skills.
- Personal install: ~/.claude/skills/
- Project install: .claude/skills/ inside your project folder
- Claude apps: Settings → Capabilities → Skills, then upload the ZIP
How do you trigger the skill with a natural request?
You do not need a special command. Describe your RL task in plain language and mention the algorithm or the reward problem. The skill's description covers reinforcement learning, RL agents, PPO, SAC and reward design, so requests that touch those topics are the ones it is meant to catch.
The skill's metadata also lists trigger keywords such as reinforcement learning, rl, policy, reward, agent, environment, ppo and sac.
- "I'm training a PPO agent on a continuous control task. What hyperparameters should I start with?"
- "Help me design a reward function for my RL agent that avoids unwanted behaviour."
- "Which algorithm should I use for a discrete-action game versus a robot arm with continuous joints?"
- "Review my RL experiment plan: how many seeds and evaluation episodes should I report?"
What does it need to run?
The skill lists no requirements. It does not run scripts and does not use the network. Because it is guidance only, you do not need to install a library to use it. You will still need your own RL environment and training code to do the actual work.
What about safety?
Because the skill runs no scripts and makes no network calls, it has no direct effect on your system. Its risk is advisory: the hyperparameters are starting points, not guarantees for your environment, so treat them as hypotheses to test rather than settings to trust blindly.
Tips for getting useful answers
Give the agent specifics. Name your environment, action type, reward signal and compute budget, and ask it to check your evaluation plan against the skill's checklist. The skill is most useful as a second opinion on an experiment you are already designing.
- Always ask for a multi-seed run before trusting a result.
- Run a small hyperparameter sweep rather than relying on one setting.
- Compare against a random policy and a simple baseline in every report.
- Check reward shaping for bias if the agent finds unexpected shortcuts.
What people use it for
- Choosing between PPO and SAC for a continuous control project.
- Getting a starting set of PPO or SAC hyperparameters before a sweep.
- Reviewing an RL experiment plan for seeds, evaluation episodes and baselines.
- Drafting the evaluation section of a research write-up with mean and standard deviation reporting.
- Learning the main algorithm families and their common pitfalls as a student.
Frequently asked questions
Is the RL Policy Optimization skill free to use?
The skill is listed under the MIT licence. The source does not mention pricing, so check the licence file in the repository for the terms that apply.
Do I need to install anything else to use this skill?
The skill lists no requirements, runs no scripts and uses no network. You still need your own RL environment and training code to run experiments.
Will this skill train my RL agent for me?
No. It only advises. It contains no code and does not build or run training.
Are the hyperparameters guaranteed to work?
No. The values such as PPO clip 0.2 and learning rate 3e-4 are typical starting points. Your environment may need a sweep to find better settings.
How many seeds should I use for RL results?
The skill's guidance says seed sensitivity is high and recommends 5 or more seeds. It also recommends reporting mean and standard deviation over 10 or more evaluation episodes.
Is this skill suitable for production systems?
Not on its own. The skill gives generic guidance for production systems that need a tested, verified training pipeline, so you should add your own testing and validation.
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Our verdict
Use this skill if you are starting an RL project, writing up experiments, or learning the algorithm families and their pitfalls, and you want a quick checklist for PPO, SAC and evaluation. Pick something else if you need a finished, tested training pipeline for production or a tool that actually builds and runs your RL experiments.
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