Lauri, Mikko ; Pajarinen, Joni ; Peters, Jan (2023)
Information Gathering in Decentralized POMDPs by Policy Graph Improvement.
18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). Montreal, Kanada (13.-17.05.2019)
doi: 10.26083/tuprints-00020576
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion

Kurzbeschreibung (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.

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