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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Reinforcement Learning: Zero to Hero!
| Date | Stars |
|---|---|
| 2026-07-24 | 2291 |
| 2026-07-25 | 2291 |
| 2026-07-28 | 2292 |
| 2026-07-30 | 2292 |
| 2026-07-31 | 2292 |
| 2026-08-02 | 2291 |
| 2026-08-04 | 2292 |
| 2026-08-06 | 2292 |
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# Deep Reinforcement Learning: Zero to Hero! [](LICENSE) [](https://www.python.org/downloads/) [](https://github.com/psf/black) Welcome to [drlzh.ai](https://drlzh.ai): a hands-on deep reinforcement learning course where you build the algorithms, not just read about them. <div style="width: 75%; margin: auto;"> <img src="assets/drlzh-splash.gif"> </div> Start from MDPs and tabular RL, then work your way to the algorithms behind Atari agents, continuous-control robots, AlphaZero-style planning, RLHF for language models, Decision Transformers, VLA-style policies, world models, Dreamer, and meta-learning. The root notebooks are the exercise track: code is intentionally replaced with guided `TODO` sections. The [`solution/`](solution/) notebooks contain the complete, runnable versions, so you can unblock yourself without leaving the course. ## Curriculum | Notebooks | Track | You build | | --------- | -------------------- | --------------------------------------------------------------------------- | | `00`-`07` | Foundations | MDPs, tabular RL, DQN, REINFORCE, actor-critic methods, DDPG, TD3, SAC, PPO | | `08`-`10` | Breaking assumptions | RND curiosity, multi-agent RL, offline RL with BC and IQL | | `11` | Planning | Monte Carlo Tree Search, self-play, AlphaZero-style policy/value learning | | `12`-`13` | Modern AI stack | RLHF with PPO, DPO, GRPO, Decision Transformers, and NanoVLA (`DTVLA`) | | `14` | Production | TensorBoard, checkpointing, debugging, multiple seeds, Ray, Optuna | | `15`-`16` | World models | MBPO with SAC, then `DR3AM`/Dreamer with RSSM latent imagination | | `17`-`18` | Meta + wrap-up | MAML, FOMAML, fast adaptation, and course conclusion | The foundations are meant to be done in order. The advanced notebooks are self-contained, but the numbering gives you a good default path from exploration to the course capstone. ## AI Companion The Docker workspace includes the **DRL-ZH AI Companion**, a VS Code extension built for this course. It knows which notebook and `TODO` you are working on, offers Socratic hints instead of spoilers, and supports text or voice mode. Bring your own LLM key: Gemini is the default, with OpenAI, Anthropic, and Groq supported too. ## Quick Start The recommended setup is Docker: it gives you code-server, the notebooks, Python `>=3.13,<3.14`, the Jupyter kernel, dependencies, and the AI Companion in one reproducible workspace. 1. Install Docker and Git, then clone this repository and `cd` into it. 2. On Linux/macOS, run `printf "UID=$(id -u)\nGID=$(id -g)\n" > .env` so files are owned by you. 3. Start the default environment: ```bash docker compose up --build -d ``` 4. Open `http://localhost:8080` in a Chromium-based browser and select the `Python (drl-zh)` kernel. 5. Open `00_Intro.ipynb` and start filling in TODOs. For NVIDIA GPU access, use: ```bash docker compose -f docker-compose.yml -f docker-compose.gpu.yml up --build -d ``` For a smaller CPU-only image, use: ```bash docker compose -f docker-compose.yml -f docker-compose.cpu.yml up --build -d ``` Prefer a native setup? See [MANUAL.md](MANUAL.md) for Python, Poetry, VS Code, and Companion instructions. ## Prerequisites You should be comfortable with Python, PyTorch basics, and the usual math behind ML: probability, statistics, linear algebra, and derivatives. The notebooks teach the RL, but they assume you can read and modify real training code. ## License MIT. See [LICENSE](LICENSE).
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1ed4a54a6bf102fb, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning