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Agent skill

Rl Policy Optimization

by aiming-lab in aiming-lab/AutoResearchClaw

Best practices for reinforcement learning policy optimization.

MITAuto-check passedAI & LLM Engineering

Install Rl Policy Optimization

skills CLI
$ npx skills add aiming-lab/AutoResearchClaw --skill rl-policy-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aiming-lab/AutoResearchClaw rl-policy-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/researchclaw/skills/builtin/domain/rl-policy-optimization .claude/skills/rl-policy-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rl-policy-optimization
GitHub stars
15k
Token cost
~329 tokens
SKILL.md length
103 words
Files
1
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Best practices for reinforcement learning policy optimization.

  • Working on RL agents
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Reinforcement learning

What it does

Rl Policy Optimization is an agent skill from aiming-lab/AutoResearchClaw. Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.

Its SKILL.md is about 330 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Reinforcement learning. The repository describes itself as: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞. The licence is MIT.

When your agent uses it

  • Working on RL agents
  • Tasks that involve Reinforcement learning

Example prompts

  • “/rl-policy-optimization”

What it can do on your machine

Read from SKILL.md and the folder at commit be4ba47. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Rl Policy Optimization loads about 329 tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 103 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~329

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aiming-lab/AutoResearchClaw at commit be4ba47, republished under its MIT licence (© aiming-lab). 103 words, ~329 tokens.

Download SKILL.mdSave it as .claude/skills/rl-policy-optimization/SKILL.md (or your agent's skills folder).
name
rl-policy-optimization
description
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
metadata.category
domain
metadata.trigger-keywords
reinforcement learning,rl,policy,reward,agent,environment,ppo,sac
metadata.applicable-stages
9,10
metadata.priority
3
metadata.version
1.0
metadata.author
researchclaw
metadata.references
Schulman et al., Proximal Policy Optimization, 2017; Haarnoja et al., Soft Actor-Critic, ICML 2018

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

© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in researchclaw/skills/builtin/domain/rl-policy-optimization of aiming-lab/AutoResearchClaw.

Open the folder on GitHubat commit be4ba47

Compare with similar skills

Rl Policy Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Rl Policy Optimization compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Rl Policy Optimization this skillaiming-lab/AutoResearchClaw15k—~329Automated safety check: PassMIT
Hugging Face LLM Trainerhuggingface/skills11k3 repos~7.2kAutomated safety check: PassApache-2.0
Train RlOpenPipe/ART11k—~2.4kAutomated safety check: PassApache-2.0
Qwopus27b Rl TrainingR6410418/Jackrong-llm-finetuning-guide1.7k—~830Automated safety check: PassApache-2.0
Fine Tuning With TrlOrchestra-Research/AI-Research-SKILLs13k7 repos~2.9kAutomated safety check: PassMIT
Optim AgentOptim-Agent/optim-agent800—~1.3kAutomated safety check: PassMIT

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Categories

Questions about Rl Policy Optimization

What does Rl Policy Optimization do?

Best practices for reinforcement learning policy optimization. Rl Policy Optimization is an agent skill from aiming-lab/AutoResearchClaw. Best practices for reinforcement learning policy optimization.

When should I use Rl Policy Optimization?

Rl Policy Optimization fits situations like: working on RL agents; tasks that involve Reinforcement learning.

How do I install Rl Policy Optimization in Claude Code?

Run `npx skills add aiming-lab/AutoResearchClaw --skill rl-policy-optimization -a claude-code`. Or copy the skill folder (researchclaw/skills/builtin/domain/rl-policy-optimization in aiming-lab/AutoResearchClaw) into .claude/skills/rl-policy-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Rl Policy Optimization in Codex?

Run `npx skills add aiming-lab/AutoResearchClaw --skill rl-policy-optimization -a codex`. Or copy the skill folder (researchclaw/skills/builtin/domain/rl-policy-optimization in aiming-lab/AutoResearchClaw) into .agents/skills/rl-policy-optimization in your project. Codex loads it when a task matches its description.

Can I use Rl Policy Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aiming-lab/AutoResearchClaw --skill rl-policy-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rl-policy-optimization, .gemini/skills/rl-policy-optimization, .github/skills/rl-policy-optimization and .opencode/skills/rl-policy-optimization in your project.

What does Rl Policy Optimization need to run?

SKILL.md names no scripts, command-line tools or credentials: Rl Policy Optimization is instructions for the agent only.

Does Rl Policy Optimization access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Rl Policy Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Rl Policy Optimization use?

Rl Policy Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rl Policy Optimization use?

About 329 tokens (SKILL.md is roughly 1.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Rl Policy Optimization?

Skills that share tags, products or a category with Rl Policy Optimization: Hugging Face LLM Trainer (huggingface/skills, 11k stars), Train Rl (OpenPipe/ART, 11k stars), Qwopus27b Rl Training (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars) and Fine Tuning With Trl (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rl Policy Optimization?

aiming-lab (a GitHub organization) maintains it in aiming-lab/AutoResearchClaw, which has 14,587 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on August 19, 2026.

Source: aiming-lab/AutoResearchClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.