Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • Safety Guarantees in Multi-agent Learning via Trapping RegionsAleksander Czechowski and Frans A. Oliehoek. Safety Guarantees in Multi-agent Learning via Trapping Regions. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), August 2023. DownloadAbstractOne of the main challenges of multi-agent learn- ing lies in establishing convergence of the algo- rithms, as, in general, a collection of individual, self-serving agents is not guaranteed to converge with their joint policy, when learning concurrently. This is in stark contrast to most single-agent envi- ronments, and sets a prohibitive barrier for deploy- ment in practical applications, as it induces uncer- tainty in long term behavior of the system. In this work, we apply the concept of trapping regions, known from qualitative theory of dynamical sys- tems, to create safety sets in the joint strategy space for decentralized learning. We propose a binary partitioning algorithm for verification that candi- date sets form trapping regions in systems with known learning dynamics, and a heuristic sampling algorithm for scenarios where learning dynamics are not known. We demonstrate the applications to a regularized version of Dirac Generative Ad- versarial Network, a four-intersection traffic con- trol scenario run in a state of the art open-source microscopic traffic simulator SUMO, and a mathe- matical model of economic competition. BibTeX Entry@inproceedings{Czechowski23IJCAI, author = {Czechowski, Aleksander and Oliehoek, Frans A.}, title = {Safety Guarantees in Multi-agent Learning via Trapping Regions}, booktitle = IJCAI23, year = 2023, month = aug, keywords = {refereed}, abstract = { One of the main challenges of multi-agent learn- ing lies in establishing convergence of the algo- rithms, as, in general, a collection of individual, self-serving agents is not guaranteed to converge with their joint policy, when learning concurrently. This is in stark contrast to most single-agent envi- ronments, and sets a prohibitive barrier for deploy- ment in practical applications, as it induces uncer- tainty in long term behavior of the system. In this work, we apply the concept of trapping regions, known from qualitative theory of dynamical sys- tems, to create safety sets in the joint strategy space for decentralized learning. We propose a binary partitioning algorithm for verification that candi- date sets form trapping regions in systems with known learning dynamics, and a heuristic sampling algorithm for scenarios where learning dynamics are not known. We demonstrate the applications to a regularized version of Dirac Generative Ad- versarial Network, a four-intersection traffic con- trol scenario run in a state of the art open-source microscopic traffic simulator SUMO, and a mathe- matical model of economic competition. } }
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