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A major bottleneck for developing general reinforcement learning agents isdetermining rewards that will yield desirable behaviors under variouscircumstances. We introduce a general mechanism for automatically specifyingmeaningful behaviors from raw pixels. In particular, we train a generativeadversarial network to produce short sub-goals represented through motiontemplates. We demonstrate that this approach generates visually meaningfulbehaviors in unknown environments with novel agents and describe how thesemotions can be used to train reinforcement learning agents.

Transferring Agent Behaviors from Videos via Motion GANs