# Information Gathering in Decentralized POMDPs by Policy Graph Improvement

## TL;DR

This paper addresses decentralized information gathering with multiple autonomous agents using Dec-POMDPs, introducing a convex reward-based approach and a novel heuristic algorithm that significantly outperforms previous methods.

## Contribution

It presents the first heuristic algorithm for Dec-POMDPs in information gathering, leveraging convex rewards and demonstrating scalability improvements.

## Key findings

- The value function is convex with convex rewards.
- The proposed heuristic solves larger problems than previous methods.
- Empirical results show significant performance gains.

## Abstract

Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest without the ability to communicate. Decentralized partially observable Markov decision processes (Dec-POMDPs) are a general, principled model well-suited for such decentralized multiagent decision-making problems. In this paper, we investigate Dec-POMDPs for decentralized information gathering problems. An optimal solution of a Dec-POMDP maximizes the expected sum of rewards over time. To encourage information gathering, we set the reward as a function of the agents' state information, for example the negative Shannon entropy. We prove that if the reward is convex, then the finite-horizon value function of the corresponding Dec-POMDP is also convex. We propose the first heuristic algorithm for information gathering Dec-POMDPs, and empirically prove its effectiveness by solving problems an order of magnitude larger than previous state-of-the-art.