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AUEB Stats Seminars 4/3/2021: Practical Distributionally Robust Markov Decision Processes using Relative Entropy by William Greenall (UCL) Forumgrstats
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AUEB Stats Seminars 4/3/2021: Practical Distributionally Robust Markov Decision Processes using Relative Entropy by William Greenall (UCL) Empty AUEB Stats Seminars 4/3/2021: Practical Distributionally Robust Markov Decision Processes using Relative Entropy by William Greenall (UCL)

Tue 2 Mar 2021 - 22:15
AUEB STATISTICS SEMINAR SERIES MARCH 2021

AUEB Stats Seminars 4/3/2021: Practical Distributionally Robust Markov Decision Processes using Relative Entropy by William Greenall (UCL) 2021_a11


William Greenall (PhD student, UCL. Supervisor: Petros Dellaportas)

Practical Distributionally Robust Markov Decision Processes using Relative Entropy

ABSTRACT

Distributionally Robust Markov Decision Processes offer a toolset for improving performance of sequential optimisation algorithms in the face of poor or particularly uncertain estimates of a transition model. The literature has focused on the use of Wasserstein distances as a tool for regulating the extent of robustness, but is not simple to use due to its lack of closed forms. On the other hand, the Kullback-Leibler divergence has been shunned as its use has heretofore implied limited flexibility. I present a method to render the KL-divergence a useful and practical tool for the construction of ambiguity sets, and build both discrete-state and continuous-state space decision processes using the formulation.

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