I made a free hobby kit that trains an artificial-intelligence game player while you sleep

Reddit r/AI_Agents Tools
open-source reinforcement-learning game-ai atari hobby-project python

Summary

A developer named Jonny released a free, open-source hobby kit that trains a reinforcement-learning AI game player on Atari 2600 and arcade games, letting users demonstrate techniques and watch the agent, nicknamed Chip, improve overnight.

TOTALLY FREE: Made as a hobby. Works with arcade and Atari 2600 games Want to see a computer master a game from scratch? I made a super easy way to create your own artificial-intelligence game player. You can set it practising, go to bed and see what it learned overnight. It works with Atari 2600 and arcade games, including Pitfall, Breakout and Space Invaders. No games are included, so you add your own game files. If you're good at a game, you can take over and show it what to do. It watches your technique and gets a massive head start. Then leave it practising and it can end up better than you. It's clever too. In Breakout it started by moving the bat randomly, then learned to drill a hole through the wall and get the ball behind the blocks. I nearly spat out my coffee when I saw it discover that by itself. It also rolls through Space Invaders levels one after the other, looping the score when it reaches the maximum. Why did I do this? I got into machine learning years ago and quickly got fed up with spending more time fixing training environments than watching anything learn. When I'm not running my little shop in Blackpool, I tinker with information technology, so I eventually built the sort of kit I'd have wanted at the time. To get going, just unzip it and run it on Windows 10 or 11. No fancy install is needed. Add your own game files and press Train. The first start downloads the dependencies automatically. Your computer learner is called Chip. At first he's completely rubbish and doesn't even know which button fires. Leave him practising and his report card tracks his scores as they climb. You can also take over and demonstrate a technique yourself. I won't bore you with the technical details here. The source and more information are on GitHub and my website, and I'll add those links in the first comment. Cheers, Jonny PS. It hasn't been tested on machines without an Nvidia graphics card yet, but I'm ready to patch anything that needs patching. Tested on three different personal computers.
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