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Information Gathering in Decentralized POMDPs by Policy Graph Improvement

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
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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Information Gathering in Decentralized POMDPs by Policy Graph Improvement
Language: English
Date: 17 October 2023
Place of Publication: Darmstadt
Year of primary publication: May 2019
Publisher: International Foundation for Autonomous Agents and Multiagent Systems
Book Title: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems
Event Title: 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019)
Event Location: Montreal, Kanada
Event Dates: 13.-17.05.2019
DOI: 10.26083/tuprints-00020576
Corresponding Links:
Origin: Secondary publication service
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.

Uncontrolled Keywords: decentralized POMDPs, multi-agent planning, planning under uncertainty, information theory
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-205764
Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Intelligent Autonomous Systems
TU-Projects: EC/H2020|640554|SKILLS4ROBOTS
Date Deposited: 17 Oct 2023 11:34
Last Modified: 23 Oct 2023 09:23
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20576
PPN: 512616507
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