A superharmonic approach to solving infinite horizon partially observable Markov decision problems

This paper employs an approach which uses a superharmonic property of a sequence of functions generated by an algorithm to show that these functions converge in a non-increasing manner to the optimal value function for the present problem, and bounds are given for the loss of optimality if the computational process is terminated at any iteration. The basic procedure is to add an additional linear term at each iteration, selected by solving a particular optimisation problem, for which primal and dual linear programming formulations are given.