A visual attention operator for playing Pac-Man

The paper explores the use of reinforcement learning in tasks that involve image-like (but not necessarily raw-pixel) state representations. It proposes a visual attention operator, related to the concept of visual attention presented in previous works. The operator is tested on the game of Pac-Man, where it allows the use of the same agent with various game layouts, no matter what their dimensions (and thus the size of the image-like representation) are. It is shown that the approach is able to summarize information present in the original representation into a fixed-size glimpse and that the approach is able to outperform several more direct approaches.