Hands-On Modern RL
A practice-first reinforcement learning course from CartPole to LLM post-training and agentic systems.
Evidence shows this is an open-source course repository with no mechanisms for permission management, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Therefore all trust criteria score 0.
Self-consistency: Information in README and package.json is consistent, course structure is clear, but no execution verification is provided. Dependency availability: Dependency list exists, but no lock file or integrity checks are provided. Failure messages: README mentions debugging and failure signals, but no specific error handling mechanisms are provided.
Audience and scenarios: README clearly lists target audience and recommended background, but no specific use cases. Capability boundaries: Course scope is clear, but limitations are not stated. Trigger precision: Not applicable as this is not an automated agent. Environment fit: Environment setup guide is provided, but cross-platform compatibility is not discussed.
Information architecture: README provides clear table of contents and chapter structure. Install notes: Environment setup guide is provided, but detailed installation steps are missing. Naming stability: Version number exists, but naming conventions are not provided. Examples and FAQ: Experiment code and course preview are provided, but no FAQ. Known limitations: README mentions some chapters are under construction, but not detailed. License: Clearly CC BY-NC-SA 4.0. Versioning and changelog: Version number and news exist, but no formal changelog. Maintenance responsibility: No explicit maintainer is specified.
Output usability: Course content is well-organized, code examples are runnable. Marginal value: Provides comprehensive curriculum from basics to frontier, educational value. Cost-benefit: No cost information, but as open-source course, cost is low.
Claim traceability: Claims in README are not supported by specific evidence. Cross-source corroboration: No external verification. Fact-inference separation: Facts and inferences are not clearly separated.
- This repository is a course project, not an executable agent product, so trust-related criteria are not applicable.
- Dependencies are not locked, posing supply chain risks.
- License is CC BY-NC-SA, restricting commercial use.
- Course content may contain errors, and some chapters are under construction.
What does this agent do, and when should you use it?
Hands-On Modern RL is an open-source course that teaches modern reinforcement learning through hands-on practice. It starts with runnable code and observable training behavior to help learners understand states, value functions, policy gradients, reward modeling, and credit assignment. The curriculum spans from classic control tasks like CartPole to frontier topics including LLM post-training, DPO/GRPO preference alignment, RLVR, multi-turn tool-use agents, Agentic RL, and vision-language model (VLM) reinforcement learning. The repository includes a VitePress documentation site, chapter-aligned code examples, build scripts, and a verification pipeline. It is designed for ML engineers, researchers, LLM practitioners, and self-learners, providing a solid ladder from basic RL to modern post-training and agent systems.
The repository provides a curriculum with 29 chapters across 7 parts, covering topics from MDPs, DQN, PPO, to RLHF, DPO, GRPO, Agentic RL, and VLM RL. It includes a VitePress-based documentation site that can be built and previewed locally. The repository offers runnable code examples organized by chapter in the code/ directory, such as code/chapter01_cartpole/1-ppo_cartpole.py. Users can start the local docs server with npm run dev, install dependencies with npm install, and install Python requirements with pip install -r requirements.txt. The course also supports verification via npm run verify which checks formatting, lints, builds, and verifies artifacts. Additionally, PDF builds for both Chinese and English are automatically released via CI.
- ML engineers moving from supervised learning to RL who want to understand core concepts through hands-on experiments.
- Researchers and students preparing to read modern RL and alignment papers like RLHF, DPO, and GRPO.
- LLM practitioners interested in RLHF, DPO, GRPO, RLVR, and post-training systems.
- Developers building tool-use agents, web agents, code agents, or evaluation pipelines.
- Self-learners who prefer code, experiments, and visual intuition before dense derivations.
- Educators looking for a practical, comprehensive RL course for teaching.
What are this agent's strengths and limitations?
- Practice-first approach with runnable code and real training curves, avoiding black-box APIs.
- Broad coverage from classic control to LLM alignment, Agentic RL, and VLM RL.
- Detailed code examples with line-by-line explanations, including PPO, DPO, and GRPO implementations.
- Bilingual (English/Chinese) support with PDF releases for offline use.
- Active community and contribution guidelines, with a clear roadmap.
- Course is still under construction; some chapters may contain errors or be incomplete.
- High computational requirements for LLM/VLM experiments; GPU access is needed and authors are seeking help.
- Multiple dependencies (Node.js, Python, PyTorch) and environment setup may be complex for beginners.
- License (CC BY-NC-SA 4.0) restricts commercial use.
- Some experiments require additional system libraries or model downloads, increasing setup time.
How do you install or deploy this agent?
Clone the repository: git clone https://github.com/walkinglabs/hands-on-modern-rl.git. Navigate into the directory and install Node dependencies: cd hands-on-modern-rl && npm install. For Python code examples, set up a virtual environment: cd code && python -m venv .venv && source .venv/bin/activate then pip install -r requirements.txt or install chapter-specific requirements. Requires Node.js >= 18 and Python 3.
How do you use this agent?
Run the docs site locally with npm run dev and open http://localhost:5173. To run a specific example, such as CartPole: python code/chapter01_cartpole/1-ppo_cartpole.py. Before submitting changes, run npm run verify to check formatting, lint, build, and artifacts. Alternatively, access the online course at https://walkinglabs.github.io/hands-on-modern-rl/. For experiments involving LLMs or VLMs, additional hardware (GPU) may be required.
FAQ
Is this course suitable for complete beginners to RL?
What hardware do I need to run the code?
How often is the course updated?
Can I contribute to the course?
npm run verify.