  A major bottleneck for developing general reinforcement learning agents is
determining rewards that will yield desirable behaviors under various
circumstances. We introduce a general mechanism for automatically specifying
meaningful behaviors from raw pixels. In particular, we train a generative
adversarial network to produce short sub-goals represented through motion
templates. We demonstrate that this approach generates visually meaningful
behaviors in unknown environments with novel agents and describe how these
motions can be used to train reinforcement learning agents.
