Scalable, MDP-based planning for multiple, cooperating agents

This paper introduces an approximation algorithm for stochastic multi-agent planning based on Markov decision processes (MDPs). Specifically, we focus on a decentralized approach for planning the actions of a team of cooperating agents with uncertainties in fuel consumption and health-related models. The core idea behind the algorithm presented in this paper is to allow each agent to approximate the representation of its teammates. Each agent therefore maintains its own planner that fully enumerates its local states and actions while approximating those of its teammates. In prior work, the authors approximated each teammate individually, which resulted in a large reduction of the planning space, but remained exponential (in n - 1 rather than in n, where n is the number of agents) in computational scalability. This paper extends the approach and presents a new approximation that aggregates all teammates into a single, abstracted entity. Under the persistent search & track mission scenario with 3 agents, we show that while resulting performance is decreased nearly 20% compared with the centralized optimal solution, the problem size becomes linear in n, a very attractive feature when planning online for large multi-agent teams.