SHP-VI Method of Solving DEC-POMDP Problem

Abstract

DEC-POMDP(Distributed Partially Observable Markov Decision Process) model is a multi-agent model of collaborative decision-making is important, but due to an alarming number of DEC-POMDP problem state space and great strategy solution space, so DEC-POMDP solution of the problem becomes very difficult. The agent from the initial state to the target state during the interaction with the environment, the system's maximum benefit is often only with some small amount of a higher reward states. This article by searching from the initial belief state to the target state to get a shortest Hamiltonian path, according to the corresponding sequence of actions on the path forward search to get faith belief state space trajectory, and then along the trajectory reverse convictions value function iteration, thus forming the state with the largest gains beliefs trajectory corresponding optimal strategy. In this paper, shortest Hamiltonian path-based value iteration to search the optimal path of faith so as to solve the state Hamiltonian larger DEC-POMDP problem.