Toward Decision Making in the Real World: From Trustworthy to Actionable

ABSTRACT

Reinforcement learning (RL) has achieved success in areas such as gaming, robotics, and language models, sparking curiosity about its applicability in the real world. When applying RL to real-world decision-making, challenges arise related to data and models. We will discuss the implications of these challenges on the feasibility of RL and share preliminary efforts to address them. These efforts include developing realistic simulators and bridging the gap between simulation and the real world through uncertainty quantification and the use of language models.

BIOGRAPHY

Hua Wei is an assistant professor at the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). He got his PhD from Pennsylvania State University in 2020. He specializes in spatio-temporal data mining, artificial intelligence, and reinforcement learning. He has been awarded the Best Paper at ECML-PKDD 2020, and his students and his own research work as a first author have been published in top conferences and journals in the fields of machine learning, artificial intelligence, data mining, and control (NeurlPS, AAAI, KDD, IJCAI, CDC, ECML-PKDD, WWW, CIKM). His research has been funded by the National Science Foundation, the Department of Energy, and the Department of Transportation.

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