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Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning

Neural Information Processing Systems

Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a propositional objectoriented framework that has provably efficient learning bounds with respect to samplecomplexity.