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Large Markov Decision Processes and Combinatorial Optimization

Ali Eshragh
Dec 2022
摘要
Markov decision processes continue to gain in popularity for modeling a widerange of applications ranging from analysis of supply chains and queuingnetworks to cognitive science and control of autonomous vehicles. Nonetheless,they tend to become numerically intractable as the size of the model growsfast. Recent works use machine learning techniques to overcome this crucialissue, but with no convergence guarantee. This note provides a brief overviewof literature on solving large Markov decision processes, and exploiting themto solve important combinatorial optimization problems.
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