Computing, School of
School of Computing: Dissertations, Theses, and Student Research
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First Advisor
Leen-Kiat Soh
Date of this Version
4-27-2026
Document Type
Thesis
Citation
Undergraduate thesis
School of Computing, University of Nebraska–Lincoln
Lincoln, Nebraska, April 27, 2026
Abstract
The field of multi-agent reinforcement learning (MARL) has made significant strides in addressing sequential decision-making problems under uncertainty. However, traditional MARL frameworks assume closed-world settings with fixed agent sets, static task distributions, and unchanging environment dynamics. This thesis presents two complementary contributions that advance the state of open-world multi-agent systems research: (1) the free-range-zoo framework, an open-source environment suite for MARL in open environments featuring dynamic agent populations, evolving task sets, and changing operational frames; and (2) the MOASEI Competition, an international benchmarking event that leverages free-range-zoo to evaluate how artificial agents handle openness in complex, partially observable domains. The free-range-zoo suite provides three benchmark domains—wildfire suppression, dynamic ridesharing, and cybersecurity defense—each exhibiting distinct dimensions of openness. The MOASEI Competition, held at AAMAS 2025, attracted international participation and demonstrated the viability of benchmarking AI policies under open-system conditions. Together, these contributions provide the research community with infrastructure, benchmarks, and empirical insights for developing robust AI systems capable of operating in real-world conditions where change and uncertainty are inherent.
Advisor: Leen-Kiat Soh
Comments
Copyright 2026, Ceferino J. Patino IV. Used by permission