On the many dimensions of Dynamic Programming based Reinforcement Learning algorithms by Prof. Bruno Scherrer


2020年1月21日 (火) 11:00 12:00


C016, Level C, Lab 1



Starting from the standard Value and Policy Iteration, I shall describe many dimensions of Dynamic Programming algorithms for solving the Reinforcement Learning Problem. I will discuss their sensitivity to errors. I will also explain the connections to some of them to somewhat recent state-of-the-art algorithms.


Bruno Scherrer has been a researcher at INRIA since 2004. He has contributed to the mathematical analysis of Dynamic Programming algorithms applied to Reinforcement Learning, in particular to approximation schemes. 



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