Factored Online Planning in Many-Agent POMDPs

TL;DR

This paper introduces a scalable online planning approach for multi-agent POMDPs that simultaneously improves value and belief estimation, enabling effective decision-making in systems with many agents.

Contribution

It presents a novel combination of weighted particle filtering, scalable belief approximation, and exploitation of agent locality for multi-agent POMDP planning.

Findings

Outperforms state-of-the-art methods with many agents

Competitive with existing methods in small-agent settings

Enhances scalability and accuracy of online planning

Abstract

In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with the number of agents, making the value and belief estimation of single-agent online planning ineffective. Prior work partially tackles value estimation by exploiting the inherent structure of multi-agent settings via so-called coordination graphs. Additionally, belief estimation methods have been improved by incorporating the likelihood of observations into the approximation. However, the challenges of value estimation and belief estimation have only been tackled individually, which prevents existing methods from scaling to settings with many agents. Therefore, we address these challenges simultaneously. First, we introduce weighted particle filtering to a sample-based online planner for MPOMDPs. Second, we present a scalable approximation of the belief. Third, we bring an approach that exploits the typical locality of agent interactions to novel online planning algorithms for MPOMDPs operating on a so-called sparse particle filter tree. Our experimental evaluation against several state-of-the-art baselines shows that our methods (1) are competitive in settings with only a few agents and (2) improve over the baselines in the presence of many agents.