Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs
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How_Graph-based.pdf
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Author(s)
Agha-mohammadi, Ali-akbar
Amato, Christopher
Vian, John
Omidshafiei, Shayegan
Liu, Shih-Yuan
How, Jonathan P
Date Issued
May 2016
Journal
IEEE International Conference on Robotics and Automation, 2016. '16 ICRA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Omidshafiei, Shayegan et al. “Graph-Based Cross Entropy Method for Solving Multi-Robot Decentralized POMDPs.” IEEE, 2016. 5395–5402.
Version
Author's final manuscript
Abstract
This paper introduces a probabilistic algorithm for multi-robot decision-making under uncertainty, which can be posed as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Dec-POMDPs are inherently synchronous decision-making frameworks which require significant computational resources to be solved, making them infeasible for many real-world robotics applications. The Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) was recently introduced as an extension of the Dec-POMDP that uses high-level macro-actions to allow large-scale, asynchronous decision-making. However, existing Dec-POSMDP solution methods have limited scalability or perform poorly as the problem size grows. This paper proposes a cross-entropy based Dec-POSMDP algorithm motivated by the combinatorial optimization literature. The algorithm is applied to a constrained package delivery domain, where it significantly outperforms existing Dec-POSMDP solution methods.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICRA.2016.7487751
