2018
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
NeurIPS 2018poster
Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a propositional object-oriented framework that has provably efficient learning bounds with respect to sample complexity. However, this framework has limitations in te…