What is it about?
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.
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This page is a summary of: A visual attention operator for playing Pac-Man, May 2018, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/elektro.2018.8398308.
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