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When playing video-games we immediately detect which entity we control and wecenter the attention towards it to focus the learning and reduce itsdimensionality. Reinforcement Learning (RL) has been able to deal with bigstate spaces, including states derived from pixel images in Atari games, butthe learning is slow, depends on the brute force mapping from the global stateto the action values (Q-function), thus its performance is severely affected bythe dimensionality of the state and cannot be transferred to other games orother parts of the same game. We propose different transformations of the inputstate that combine attention and agency detection mechanisms which both havebeen addressed separately in RL but not together to our knowledge. We proposeand benchmark different architectures including both global and local agencycentered versions of the state and also including summaries of thesurroundings. Results suggest that even a redundant global-local state networkcan learn faster than the global alone. Summarized versions of the state lookpromising to achieve input-size independence learning.

Speeding up reinforcement learning by combining attention and agency features