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Information Gathering in Decentralized POMDPs by Policy Graph Improvement

Mikko Lauri; Joni Pajarinen; Jan Peters
In: Edith Elkind; Manuela Veloso; Noa Agmon; Matthew E. Taylor (Hrsg.). AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19), May 13-17, Montreal, QC, Canada, Pages 1143-1151, ISBN 978-1-4503-6309-9, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2019.


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.

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