Transition-independent decentralized markov decision processes

  • Authors:

  • Raphen Becker;Shlomo Zilberstein;Victor Lesser;Claudia V. Goldman

  • Affiliations:

  • University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA

  • Venue:
  • AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multi-agent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXP-hard, provides a partial explanation. To overcome this complexity barrier, we identify a general class of transition-independent decentralized MDPs that is widely applicable. The class consists of independent collaborating agents that are tied together through a global reward function that depends upon both of their histories. We present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact and approximate solutions.