What is reinforcement-learning?

Guidance for reinforcement learning, a method where an agent learns by trying actions in an environment and receiving rewards or penalties, using Python tools.

Use it to create Gymnasium environments, train or evaluate agents with Stable-Baselines3 or RLlib, tune hyperparameters, and deploy trained policies.

It helps avoid common mistakes when building environments, choosing algorithms, training agents, tuning settings, and diagnosing unstable learning.

reinforcement-learning — Skill for Claude Code by Aznatkoiny

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add Aznatkoiny/zAI-Skills --skill reinforcement-learning

Clone the repo

git clone --depth 1 https://github.com/Aznatkoiny/zAI-Skills

Made for: Claude Code.

Or install ai-toolkit, the plugin that ships this one along with the rest of its 6 skills, 4 commands, 2 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/reinforcement-learning/github.svg)](https://agentmods.dev/skills/aznatkoiny/zai-skills/reinforcement-learning)

Your own site

<a href="https://agentmods.dev/skills/aznatkoiny/zai-skills/reinforcement-learning"><img src="https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/reinforcement-learning/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/aznatkoiny/zai-skills/reinforcement-learning"><img src="https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/reinforcement-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,008 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00086	$0.02008
Opus 5	$0.00043	$0.01004
Sonnet 5	$0.00017	$0.00402
Haiku 4.5	$0.00009	$0.00201

Measured 9d ago against content hash 0637e1386d08, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

reinforcement-learning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/eval_report.py, scripts/pretrain_check.py, scripts/validate_env.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AI-Toolkit/skills/reinforcement-learning/SKILL.md · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reinforcement Learning Best Practices

Overview

This skill provides comprehensive guidance for implementing reinforcement learning in Python using the modern ecosystem (Gymnasium >= 1.0, Stable-Baselines3 >= 2.x). Gymnasium has replaced OpenAI Gym as the standard environment interface. Stable-Baselines3 (SB3) is recommended for prototyping, RLlib for production/distributed training, and CleanRL for research.

When to Use

Building RL agents for discrete or continuous control tasks
Creating custom simulation environments
Tuning hyperparameters for RL algorithms
Debugging training issues (reward curves, policy collapse, numerical instability)
Deploying trained policies to production

Library Selection

Library	Best For	Ease	Flexibility	Production
Stable-Baselines3	Prototyping, learning	High	Medium	Good
RLlib	Production, distributed	Medium	High	Excellent
CleanRL	Research, understanding	High	Low	Poor
TorchRL	Custom implementations	Low	Highest	Good

Algorithm Decision Tree

Start
  |
  v
Action space type?
  |
  +-- Discrete --> Sample efficiency critical?
  |                  |
  |                  +-- Yes --> DQN (or Double/Dueling DQN)
  |                  +-- No  --> Stability critical?
  |                               |
  |                               +-- Yes --> PPO
  |                               +-- No  --> A2C (faster iterations)
  |
  +-- Continuous --> Sample efficiency critical?
                       |
                       +-- Yes --> SAC (auto entropy) or TD3
                       +-- No  --> PPO (more stable, less efficient)

Quick Selection Table:

Scenario	Recommended	Why
Discrete actions, getting started	PPO	Stable, good defaults
Continuous control	SAC or TD3	Sample efficient, handles continuous well
Sample efficiency critical	SAC, DQN	Off-policy, reuses experience
Stability critical	PPO	Trust region, consistent
High-dimensional obs (images)	PPO + CNN	Handles visual input well
Fast iteration needed	A2C	Simpler, faster per update

Read the full file on GitHub · 219 lines

Files

What ships with it

9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

9d ago First seen · 219 lines · 86 tokens per session scan A 0637e1386d08

Subscribe to this mod's changes

reinforcement-learning is a skill published in the GitHub repository Aznatkoiny/zAI-Skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 2,008 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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