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The ability to plan and execute goal specific actions in varied, unexpectedsettings is a central requirement of intelligent agents. In this paper, weexplore how an agent can be equipped with an internal model of the dynamics ofthe external world, and how it can use this model to plan novel actions byrunning multiple internal simulations ("visual imagination"). Our modelsdirectly process raw visual input, and use a novel object-centric predictionformulation based on visual glimpses centered on objects (fixations) to enforcetranslational invariance of the learned physical laws. The agent gatherstraining data through random interaction with a collection of differentenvironments, and the resulting model can then be used to plan goal-directedactions in novel environments that the agent has not seen before. Wedemonstrate that our agent can accurately plan actions for playing a simulatedbilliards game, which requires pushing a ball into a target position or intocollision with another ball.

Learning Visual Predictive Models of Physics for Playing Billiards