The task is to train an agent to play SNES games such as Super Mario.
( Image credit: Large-Scale Study of Curiosity-Driven Learning )
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However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent.
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This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.