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Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Jiachen Li · Shuo Cheng · Zhenyu Liao · Huayan Wang · William Yang Wang · Qinxun Bai
Event URL: https://openreview.net/forum?id=H7Z5EuYbjw »
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration. Following the principle of $\textit{optimism in the face of uncertainty}$ (OFU), we train a separate exploration policy to maximize the approximate upper confidence bound of the critics in an off-policy actor-critic framework. However, this introduces extra differences between the replay buffer and the target policy regarding their stationary state-action distributions. To mitigate the off-policy-ness, we adapt the recently introduced DICE framework to learn a distribution correction ratio for off-policy RL training. In particular, we correct the training distribution for both policies and critics. Empirically, we evaluate our proposed method in several challenging continuous control tasks and show superior performance compared to state-of-the-art methods. We also conduct extensive ablation studies to demonstrate the effectiveness and rationality of the proposed method.

Author Information

Jiachen Li (University of California, Santa Barbara)

Jiachen Li is a second-year Ph.D. student at UC Santa Barbara working with Prof. William Wang. I received my M.S. degree in Electrical and Computer Engineering at UC San Diego, advised by Prof. Hao Su and Prof. Pengtao Xie, and my B.E. degree from Huazhong University of Science and Technology as an Outstanding Undergraduate in Terms of Academic Performance (Top 1%).

Shuo Cheng (Georgia Institute of Technology)
Zhenyu Liao (Amazon Advertising)
Huayan Wang (Kuaishou Technology)
William Yang Wang (University of California, Santa Barbara)

William Wang is the Co-Director of UC Santa Barbara's Natural Language Processing group and Center for Responsible Machine Learning. He is the Duncan and Suzanne Mellichamp Chair in Artificial Intelligence and Designs, and an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara. He received his PhD from School of Computer Science, Carnegie Mellon University. He has broad interests in Artificial Intelligence, including statistical relational learning, information extraction, computational social science, dialog & generation, and vision. He has published more than 100 papers at leading NLP/AI/ML conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, EMNLP 2015, and CVPR 2019, a DARPA Young Faculty Award (Class of 2018), an IEEE AI's 10 to Watch Award (Class of 2020), an NSF CAREER Award (2021), two Google Faculty Research Awards (2018, 2019), three IBM Faculty Awards (2017-2019), two Facebook Research Awards (2018, 2019), an Amazon AWS Machine Learning Research Award, a JP Morgan Chase Faculty Research Award, an Adobe Research Award in 2018, and the Richard King Mellon Presidential Fellowship in 2011. He frequently serves as an Area Chair or Senior Area Chair for NAACL, ACL, EMNLP, and AAAI. He is an elected member of IEEE Speech and Language Processing Technical Committee (2021-2023) and a member of ACM Future of Computing Academy. In addition to research, William enjoys writing scientific articles that impact the broader online community. His work and opinions appear at major tech media outlets such as Wired, VICE, Scientific American, Fortune, Fast Company, NASDAQ, The Next Web, Law.com, and Mental Floss.

Qinxun Bai (Horizon Robotics)

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