Jia (Kevin) Liu, PhD
Associate Professor of Electrical and Computer Engineering, Ohio State University
Thursday, June 26, 2025 | Seminar: 2 pm. | 230 A Davis Hall
Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This paper tackles the multi-objective reinforcement learning (MORL) problem and introduces an innovative actor-critic algorithm named MOAC which finds a policy by iteratively making trade-offs among conflicting reward signals. Notably, we provide the first analysis of finite-time Pareto-stationary convergence and corresponding sample complexity in both discounted and average reward settings. Our approach has two salient features: (a) MOAC mitigates the cumulative estimation bias resulting from finding an optimal common gradient descent direction out of stochastic samples. This enables provable convergence rate and sample complexity guarantees independent of the number of objectives; (b) with proper momentum coefficient, MOAC initializes the weights of individual policy gradients using samples from the environment, instead of manual initialization. This enhances the practicality and robustness of our algorithm. Finally, experiments conducted on a real-world dataset validate the effectiveness of our proposed method.
Jia (Kevin) Liu is an associate professor in the Dept. of Electrical and Computer Engineering at The Ohio State University (OSU) and an Amazon Scholar with Amazon.com. He received his PhD degree from the Dept. of Electrical and Computer Engineering at Virginia Tech in 2010. From Aug. 2017 to Aug. 2020, he was an assistant professor in the Dept. of Computer Science at Iowa State University (ISU). He currently serves as the managing director of the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE) at OSU.
Liu's research areas include theoretical machine learning, stochastic network optimization and control, and performance analysis for data analytics infrastructure and cyber-physical systems. He is a senior member of IEEE and a member of ACM. He has received numerous best paper awards at top venues, including IEEE INFOCOM'19 Best Paper Award, IEEE INFOCOM'16 Best Paper Award, IEEE INFOCOM'13 Best Paper Runner-up Award, IEEE INFOCOM'11 Best Paper Runner-up Award, and IEEE ICC'08 Best Paper Award. He has also received multiple honors of long/spotlight presentations at top machine learning conferences, including ICML, NeurIPS, and ICLR.
His joint work with IBM Research was selected to receive the IBM Pat Goldberg Memorial Best Paper Award Distinction of Honorable Mention in 2024. Liu is an NSF CAREER Award recipient in 2020, a winner of the DARPA Young Faculty Award (YFA) in 2024, and a winner of the Google Faculty Research Award in 2020. He received the LAS Award for Early Achievement in Research at Iowa State University in 2020, and the Bell Labs President Gold Award.
Liu is the lead editor of the Special Issue on AI and Networking of IEEE/ACM Transactions on Networking in 2025. He is an associate editor for IEEE Transactions on Cognitive Communications and Networking. He has served the TPC for numerous top conferences, including ICML, NeurIPS, ICLR, ACM SIGMETRICS, IEEE INFOCOM, and ACM MobiHoc. His research is supported by NSF, DARPA, AFOSR, AFRL, ONR, Google, Meta, and Cisco.