Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs
Author(s)Agha-mohammadi, Ali-akbar; Amato, Christopher; Vian, John; Omidshafiei, Shayegan; Liu, Shih-Yuan; How, Jonathan P; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
IEEE International Conference on Robotics and Automation, 2016. '16 ICRA
Institute of Electrical and Electronics Engineers (IEEE)
Omidshafiei, Shayegan et al. “Graph-Based Cross Entropy Method for Solving Multi-Robot Decentralized POMDPs.” IEEE, 2016. 5395–5402.
Author's final manuscript