Multiagent Planning with Factored MDPs

We present a principled and efficient planning algorithm for cooperative multiagent 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 multiagent system as a single, large Markov decision process (MDP), which we assume can be represented in a factored way using a dynamic Bayesian network (DBN). The action space 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 a simple and efficient method for computing such an approximate value function by solving a single linear program, whose size is determined by the interaction be...

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Carlos Guestrin, Daphne Koller, Ronald Parr
• Keywords: Action Space, Function Approximation Architecture, NIPS 2001, NIPS 2007, Value Function