  We present Mask Atari, a new benchmark to help solve partially observable
Markov decision process (POMDP) problems with Deep Reinforcement Learning
(DRL)-based approaches. To achieve a simulation environment for the POMDP
problems, Mask Atari is constructed based on Atari 2600 games with
controllable, moveable, and learnable masks as the observation area for the
target agent, especially with the active information gathering (AIG) setting in
POMDPs. Given that one does not yet exist, Mask Atari provides a challenging,
efficient benchmark for evaluating the methods that focus on the above problem.
Moreover, the mask operation is a trial for introducing the receptive field in
the human vision system into a simulation environment for an agent, which means
the evaluations are not biased from the sensing ability and purely focus on the
cognitive performance of the methods when compared with the human baseline. We
describe the challenges and features of our benchmark and evaluate several
baselines with Mask Atari.
