Institute for Disciplinary Information Sciences
Cooperation is key to the success of humankind. As the scale of complexity increases in AI applications, we must endow AI agents with similar cooperative abilities to achieve beyond what they can do alone. In this talk, I will discuss how AI agents can autonomously learn to cooperate and coordinate with other agents. I will first introduce a cooperative multi-agent reinforcement learning (MARL) framework with factorization structures and provide formal analyses to reveal its theoretical properties. Based on these theoretical insights, I will present novel factored MARL methods that achieve state-of-the-art performance in challenging benchmarks. I will then briefly discuss approaches that are built upon factored MARL for addressing practical challenges of cooperative MARL, including learning efficiency, partial observability, and coordinated exploration. I will finish the talk by outlining some promising directions of future work.
Chongjie Zhang is an Assistant Professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University. He is the Director of the Machine Intelligence Group at IIIS. His research interest lies in studying a fundamental aspect of artificial intelligence – how intelligent agents learn to make decisions and perform actions to achieve their goals. His research spans deep reinforcement learning, multi-agent systems, and human-AI interaction; particularly, their intersections to enable agents to learn to collaborate with other agents or humans. Chongjie is also interested in applying his research to real-world applications such as clean energy production, UAV urban delivery, and finance. Before joining Tsinghua University, he received his Ph.D. from the University of Massachusetts at Amherst and worked as a postdoc in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
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