Indistinguishable Agents in Decentralised POMDPs

Karabulut, Nazlı Nur; Braun, Tanya

Abstract

Multi-agent decision making under uncertainty can be modelled using decentralised partially observable Markov decision processes (DecPOMDPs). However, DecPOMDPs are notoriously difficult to solve due to their exponential dependence on the number of agents. Lifting is a technique that treats groups of indistinguishable instances through representatives if possible, yielding tractable inference in the number of instances in a model. This paper discusses the assumptions necessary for indistinguishability among agents, providing a definition of so-called isomorphic DecPOMDPs that have a model complexity no longer dependent on the number of agents, as well as a lifted solution approach that exploits this lifted agent set for space and runtime gains.