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Adaptive Resolving Methods for Markov Decision Processes with Function Approximations

This work considers the MDP problems with function approximation with function approximation and develops a new algorithm to solve it efficiently, based on a linear programming (LP) reformulation and repeatedly resolves the identified reduced linear system as new transition samples arrive.

Jia-Shuo Jiang, Yinyu Ye, Yiming Zong · 0 citations

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