Learning in structured MDPs with convex cost functions: Improved regret bounds for inventory management

10 May 2019Shipra AgrawalRandy Jia

We consider a stochastic inventory control problem under censored demands, lost sales, and positive lead times. This is a fundamental problem in inventory management, with significant literature establishing near-optimality of a simple class of policies called ``base-stock policies'' for the underlying Markov Decision Process (MDP), as well as convexity of long run average-cost under those policies... (read more)

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