Information Gathering in Decentralized POMDPs by Policy Graph Improvement
This addresses the problem of scalable decentralized decision-making for multiagent systems in information gathering tasks, representing an incremental advance with specific performance gains.
The paper tackles decentralized information gathering by multiple agents without communication, modeling it as a Dec-POMDP with rewards based on state information like negative Shannon entropy. It proposes a heuristic algorithm that solves problems an order of magnitude larger than previous state-of-the-art.
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.