Learning from the memory of Atari 2600

Abstract

We train a number of neural networks to play the games Bowling, Breakout and Seaquest using information stored in the mem-ory of a video game console Atari 2600. We consider four models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen. As the benchmark we used the convolutional model proposed in [17] and received comparable results in all considered games. Quite surpris-ingly, in the case of Seaquest we were able to train RAM-only agents which behave better than the benchmark screen-only agent. Mixing screen and RAM did not lead to an improved performance comparing to screen-only and RAM-only agents.