The Complexity of Decentralized Control of Markov Decision Processes

We consider decentralized control of Markov decision processes and give complexity bounds on the worst-case running time for algorithms that find optimal solutions. Generalizations of both the fullyobservable case and the partially-observable case that allow for decentralized control are described. For even two agents, the finite-horizon problems corresponding to both of these models are hard for nondeterministic exponential time. These complexity results illustrate a fundamental difference between centralized and decentralized control of Markov decision processes. In contrast to the problems involving centralized control, the problems we consider provably do not admit polynomial-time algorithms. Furthermore, assuming EXP = NEXP, the problems require super-exponential time to solve in the worst case.

• Type: Conference
• Year: 2000
• Where: UAI
• Authors: Daniel S. Bernstein, Shlomo Zilberstein, Neil Immerman
• Keywords: Decentralized Control, Markov Decision Processes, UAI 2000, UAI 2008, Worst-case Running Time