Papers › An Object-Oriented Representation for Efficient Reinforcement Learning

An Object-Oriented Representation for Efficient Reinforcement Learning

1 Jul 2008ICML '08: Proceedings of the 25th international conference on Machine learning 2008 7archive 2025-07-28

Carlos Diuk, Andre Cohen, Michael 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. We introduce a learning algorithm for deterministic OO-MDPs and prove a polynomial bound on its sample complexity. We illustrate the performance gains of our representation and algorithm in the well-known Taxi domain, plus a real-life videogame.

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borea17/efficient_rl MIT report

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Object Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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