An Object-Oriented Representation for Efficient Reinforcement Learning

ICML '08: Proceedings of the 25th international conference on Machine learning 2008 Carlos DiukAndre CohenMichael L. Littman

Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object-Oriented MDPs (OO-MDPs), a representation based on objects and their interactions, which is a natural way of modeling environments and offers important generalization opportunities... (read more)

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