α-min: a compact approximate solver for finite-horizon POMDPs

Abstract

In many POMDP applications in computational sustainability, it is important that the computed pol-icy have a simple description, so that it can be easily interpreted by stakeholders and decision makers. One measure of simplicity for POMDP value func-tions is the number of α-vectors required to repre-sent the value function. Existing POMDP methods seek to optimize the accuracy of the value func-tion, which can require a very large number of α-vectors. This paper studies methods that allow the user to explore the tradeoff between the accu-racy of the value function and the number of α-vectors. Building on previous point-based POMDP solvers, this paper introduces a new algorithm (α-min) that formulates a Mixed Integer Linear Pro-gram (MILP) to calculate approximate solutions for finite-horizon POMDP problems with limited num-bers of α-vectors. At each time-step, α-min cal-culates α-vectors to greedily minimize the gap be-tween current upper and lower bounds of the value function. In doing so, good upper and lower bounds are quickly reached allowing a good approximation of the problem with few α-vectors. Experimental results show that α-min provides good approximate solutions given a fixed number of α-vectors on small benchmark problems, on a larger randomly generated problem, as well as on a computational sustainability problem to best manage the endan-gered Sumatran tiger.