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
Conference or Workshop Item, Secondary publication, Publisher's Version
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Item Type: | Conference or Workshop Item |
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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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