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We present Mask Atari, a new benchmark to help solve partially observableMarkov decision process (POMDP) problems with Deep Reinforcement Learning(DRL)-based approaches. To achieve a simulation environment for the POMDPproblems, Mask Atari is constructed based on Atari 2600 games withcontrollable, moveable, and learnable masks as the observation area for thetarget agent, especially with the active information gathering (AIG) setting inPOMDPs. 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 inthe human vision system into a simulation environment for an agent, which meansthe evaluations are not biased from the sensing ability and purely focus on thecognitive performance of the methods when compared with the human baseline. Wedescribe the challenges and features of our benchmark and evaluate severalbaselines with Mask Atari.

Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks