Decentralized Control of Partially Observable Markov Decision Processes Using Belief Space Macro-Actions
Author(s)Omidshafiei, Shayegan; Agha-mohammadi, Ali-akbar; Amato, Christopher; How, Jonathan P.
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he focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 2015 IEEE International Conference on Robotics and Automation
Institute of Electrical and Electronics Engineers (IEEE)
Omidshafiei, Shayegan, Ali-akbar Agha-mohammadi, Christopher Amato, and Jonathan P. How. "Decentralized Control of Partially Observable Markov Decision Processes Using Belief Space Macro-Actions." 2015 IEEE International Conference on Robotics and Automation (May 2015).
Author's final manuscript