Multi-Agent Decentralized Belief Propagation on Graphs

TL;DR

This paper introduces a decentralized belief propagation algorithm for networked multi-agent I-POMDPs, enabling agents to make decisions based on local observations and neighbor messages to optimize global returns.

Contribution

It presents the first decentralized belief propagation method for multi-agent I-POMDPs, with proven convergence and multiple practical applications.

Findings

Algorithm converges under specified conditions

Effective in various multi-agent network scenarios

Enhances decision-making in decentralized systems

Abstract

We consider the problem of interactive partially observable Markov decision processes (I-POMDPs), where the agents are located at the nodes of a communication network. Specifically, we assume a certain message type for all messages. Moreover, each agent makes individual decisions based on the interactive belief states, the information observed locally and the messages received from its neighbors over the network. Within this setting, the collective goal of the agents is to maximize the globally averaged return over the network through exchanging information with their neighbors. We propose a decentralized belief propagation algorithm for the problem, and prove the convergence of our algorithm. Finally we show multiple applications of our framework. Our work appears to be the first study of decentralized belief propagation algorithm for networked multi-agent I-POMDPs.