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