What it does Eight classic games—Snake, Tetris, Pong, Flappy Bird, Pac-Man, and others—each with one to three RL algorithms bolted on. The code lives in separate subdirectories, so you can cherry-pick whatever toy problem you’re currently trying to understand.

The interesting bit The Chrome T-Rex game gets three algorithms while most others get one, suggesting someone really wanted that dinosaur to jump. The Chinese translations in the table imply this started as a personal learning project that grew into a reference collection.

Key highlights

• Covers the hits: DQN on Snake, Tetris, Pong, etc.
• Subprojects range from 1–3 implemented algorithms each
• Pure Python, no dependency manifest visible in the README
• WeChat blog tie-in for Chinese-language walkthroughs
• 607 stars suggests it resonates as a teaching repo

Caveats

• README is a table of contents and little else; no install instructions, no requirements.txt mentioned
• “2 algorithms” for Snake doesn’t name them—you’ll need to click through to find out
• No benchmarks or training curves shown

Verdict Good if you’re learning RL and want working code for familiar games. Skip if you need a maintained framework or reproducible experiments out of the box.