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Hierarchical model-based reinforcement learning
2008
Proceedings of the 25th international conference on Machine learning - ICML '08
Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying structure. Model-based algorithms, which provided the first finite-time convergence guarantees for reinforcement learning, may also play an important role in coping with the relative scarcity of data in large environments. In this paper, we introduce an algorithm that fully integrates modern hierarchical and model-learning methods in the standard
doi:10.1145/1390156.1390211
fatcat:hv6i4sncsnfyfayvgqega7lh24