Description
First things first: playing good old Atari games might be cool but why should I write a program for doing it? Well teaching a computer to play a game means teaching it to develop strategies and use foresight planning to solve a certain problem. The tools you gather while solving i.e. space invaders are the same you may use to solve any problem which requires a sequential set of decisions in order to find an optimal solution to some problem, like i.e. controlling a robot that collects garbage. Furthermore, there is a lot of scientific research on reinforcement learning that focuses on solving Atari games which makes it a good starting point, as large amounts of publications and open source code already exists.
What to expect from this talk? At first there will be a very short introduction to reinforcement learning theory, just the very basics, common applications and some references for further reading. Next points are, how to run Atari games from inside python for a learning task (with OpenAI's gym), and where to find an algorithm for the actual learning problem. Finally it will be shown how to build it all together in a jupyter notebook and let the algorithm play the game. Et voilà that's your computer beating you in space invaders.