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Learning from the Memory of Atari 2600

2017; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-57969-6_6

1865-0937

Jakub Sygnowski, Henryk Michalewski,

Artificial Intelligence in Games

We train a number of neural networks to play the games Bowling, Breakout and Seaquest using information stored in the memory 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 surprisingly, 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.