53 citations · 114 across the 11 of their papers we have counts for
11 papers
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Jiachen Li, Edwin Zhang, Ming Yin +3
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a poli…
Generalized Tangent Kernel: A Unified Geometric Foundation for Natural Gradient and Standard Gradient
Qinxun Bai, Steven Rosenberg, Wei Xu
Natural gradients have been widely studied from both theoretical and empirical perspectives, and it is commonly believed that natural gradients have advantages over standard (Eucli…
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Jiachen Li, Shuo Cheng, Zhenyu Liao +3
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration. Following the principle of (OFU)…
Generative Particle Variational Inference via Estimation of Functional Gradients
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference…
Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers
Qi Feng, Vitaly Ablavsky, Qinxun Bai +1
We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The propose…
Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Efficient exploration remains a challenging problem in reinforcement learning, especially for those tasks where rewards from environments are sparse. A commonly used approach for e…