walkinglabs/hands-on-modern-rl resource

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4124 stars · 297 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An open-source hands-on curriculum/book covering modern reinforcement learning, from MDPs and policy optimization (PPO) to LLM alignment (RLHF, DPO, GRPO, RLVR) and agentic systems. It includes experiment code, notebooks, and online environments, published as a VitePress site and PDF under CC BY-NC-SA 4.0.

Use cases

• learn reinforcement learning from scratch with hands-on code
• understand how RLHF and DPO align LLMs
• study GRPO and RLVR for reasoning models
• find a tutorial bridging classic RL to agentic AI
• run CartPole and classic RL experiments in notebooks
• learn policy optimization with PyTorch

When to choose

• you want a structured, code-driven path from basic RL to LLM alignment and agents
• you prefer runnable notebooks and online experiments alongside theory
• you need coverage of modern post-training methods like GRPO, DPO, and RLVR

When to avoid

• you need a production RL training framework rather than a learning resource
• you require rigorously peer-reviewed content - the course is AI-assisted and not fully reviewed
• you need a permissively licensed codebase - content is CC BY-NC-SA (non-commercial)

Facets

learning-resource · maturity active

reinforcement-learning llm-training agent-framework machine-learning reinforcement-learning large-language-models tutorials education python cross-platform rlhf grpo dpo ppo rlvr llm-alignment curriculum pytorch open-course ai-agents

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