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Dissertation

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Jiaxun Cui · Electrical and Computer Engineering Advisor: Peter Stone The University of Texas at Austin, 2025 Multi-agent learning aims to allow artificial intelligence (AI) agents to learn from interactions with other agents in an environment. However, as AI increasingly integrates into real-world systems, significant challenges arise in how to robustly interact with and communicate with a variety of other agents, particularly in complex environments such as autonomous driving, where humans and AI agents coexist. This dissertation research investigates how agents can be trained to effectively communicate with and generalize to diverse partners (including humans) in simulated real-world scenarios. Towards addressing this challenge, this dissertation explores three key dimensions: (1) learning communication-supporting representations that facilitate coordination, (2) developing multi-agent policies that generalize to new teammates or opponents, and (3) learning to collaborate with human

Ph.D. Dissertation · UT Austin Communication and Generalization in Multi-Agent Learning Jiaxun Cui · Electrical and Computer Engineering Advisor: Peter Stone The University of Texas at Austin, 2025 Download Manuscript (PDF) Defense Slides Back to homepage Abstract Multi-agent learning aims to allow artificial intelligence (AI) agents to learn from interactions with other agents in an environment. However, as AI increasingly integrates into real-world systems, significant challenges arise in how to robustly interact with and communicate with a variety of other agents, particularly in…

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