Navigating and Stopping Multi-Parameter Bandit Processes

Abstract

In recent years, considerable effort has been devoted to the development of a theory for multi-parameter processes. These are stochastic processes that evolve in “time” which is only partially ordered. The multi-parameter theory provides a natural way to formulate problems in dynamic allocation of resources, including discrete and continuous time multi-armed bandits as special cases. Multi-parameter processes that describe a game played by a gambler against a multi-armed bandit are called bandit processes. My talk will focus on two control problems for bandit processes. The first problem, the optimal stopping problem, is that of a gambler who can stop playing at any time. The reward from the game depends only on the state of affairs at the time of stopping, and the gambler’s problem is to choose an optimal stopping time. In the second problem, the optimal navigation problem, the gambler plays forever and seeks to maximize total discounted reward over an infinite horizon.