Learning from the memory of Atari 2600
This work addresses game-playing AI by exploring alternative data sources, but it is incremental as it builds on existing benchmarks without major breakthroughs.
The researchers tackled the problem of training neural networks to play Atari 2600 games using console memory (RAM) data, achieving comparable results to a benchmark screen-only model in games like Bowling, Breakout, and Seaquest, with RAM-only agents outperforming the benchmark in Seaquest.
We train a number of neural networks to play 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 NIPS 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.