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The recent adaptation of deep neural network-based methods to reinforcementlearning and planning domains has yielded remarkable progress on individualtasks. Nonetheless, progress on task-to-task transfer remains limited. Inpursuit of efficient and robust generalization, we introduce the SchemaNetwork, an object-oriented generative physics simulator capable ofdisentangling multiple causes of events and reasoning backward through causesto achieve goals. The richly structured architecture of the Schema Network canlearn the dynamics of an environment directly from data. We compare SchemaNetworks with Asynchronous Advantage Actor-Critic and Progressive Networks on asuite of Breakout variations, reporting results on training efficiency andzero-shot generalization, consistently demonstrating faster, more robustlearning and better transfer. We argue that generalizing from limited data andlearning causal relationships are essential abilities on the path towardgenerally intelligent systems.

Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics