alessiodm/drl-zh resource

Health v2 · maintenance only

Adoption not part of the score

2293 stars · 115 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A hands-on deep reinforcement learning course delivered as Jupyter notebooks, taking learners from MDPs and tabular RL to modern methods like RLHF, AlphaZero-style planning, world models, and meta-learning. Exercise notebooks contain guided TODOs with full solutions provided, plus a Docker workspace with an AI companion VS Code extension.

Use cases

• learn deep reinforcement learning from scratch
• implement DQN, PPO, and SAC myself instead of reading about them
• understand how RLHF and DPO train language models
• study AlphaZero-style planning and Monte Carlo Tree Search
• learn world models like Dreamer with worked code
• practice RL with guided exercises and solutions
• get hands-on with multi-agent and offline RL

When to choose

• you want to build RL algorithms from first principles with guided exercises
• you want a structured curriculum covering both classic and cutting-edge RL including RLHF and world models
• you prefer runnable notebooks with solutions when stuck

When to avoid

• you need a production-ready RL library rather than educational code
• you want a video course or textbook instead of notebooks
• you need GPU-cluster-scale training out of the box

Facets

learning-resource · maturity active

machine-learning reinforcement-learning deep-learning reinforcement-learning machine-learning deep-learning tutorials python cross-platform jupyter-notebooks course rlhf world-models alphazero hands-on-learning exercises docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="alessiodm/drl-zh")