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Daphne Koller Publications

Multiagent Planning with Factored MDPs (2002)

by C.E. Guestrin, D. Koller, and R. Parr

Abstract: We present a new, principled and efficient planning algorithm for cooperative multi-agent dynamic systems. A striking feature of our method is that the coordination and communication between the agents is not imposed, but derived directly from the system dynamics and function approximation architecture. We view the entire multi-agent system as a single large Markov decision process (MDP), which we assume canb e represented in a factored way using a dynamic Bayesian network (DBN). THe actionspace of the resulting MDP is the joint action space of the entire set of agents. Our approach is based on the use of factored linear value functions as an approximation to the joint value function. This factorization of the value function allows the agents to coordinate their actions at runtime using a natural message passing scheme. We provide asimple and efficient method for determining such an approximate value function by solving a single linear program, whose size is determined by the interaction between the value function structure and the DBN, avoiding t he exponential blowup in the state and action space. We show that our approach compares favorably with approaches based on reward sharing. We alsoshow that our algorithm is an efficient alternative to more complicated algorithms even in the single agent case.

Download Information

C.E. Guestrin, D. Koller, and R. Parr (2002). "Multiagent Planning with Factored MDPs." Advances in Neural Information Processing Systems (NIPS 2001) (pp. 1523-1530). pdf

Bibtex citation

  author =       "C.E. Guestrin and D. Koller and R. Parr",
  booktitle = {Advances in Neural Information Processing Systems (NIPS 2001)}, 
  title =        "Multiagent Planning with Factored {MDP}s",
  address =      "Vancouver, Canada",
  pages =        "1523--1530",
  year =         "2002",

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