My talk is going to be a hands on, on how to build a pong playing AI, using Q-learning, step by step. Unfortunately training the agents even for very simple games still takes ages and I really wanted to have something training while I do the talk, so I've built two little games that I hope should train a bit faster.
This a version of pong with some of visual noise stripped out, no on screen score, no lines around the board. Also when you start you can pass args for the screen width and height and the game play should scale with these. This means you can run it as an 80x80 size screen(or even 40x40) and save to having to do the downsizing of the image when processing.
google deepmind report doing. Possibly they are using other tricks not reported in the paper, or just lots of hyper parameter tuning, or there are still more bugs in my implementation(entirely possible, if anyone finds any please submit).
I've also checked in some checkpoints of a trained half pong player, if anyone just wants to quickly see it running. Simply run this, from the examples directory.
It performs significantly better than random, though still looks pretty bad compared to a human.
Distance from building our future robot overlords, still significant.