[email protected] Abstract Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning ... WebTime: June 18th, 2024 15:00Locaiton: N412, Mong Man-wei Science Technology BuildingAt the heart of Reinforcement Learning lies the challenge of trading exploration -- collecting …
Jianyu Chen
WebMy research interests include Reinforcement Learning and Deep Learning. My thesis is to improve the sample efficiency of reinforcement learning via inductive models including object-oriented representation model, plannable world model, and associative memory model, and I won the award for Excellent Doctoral Dissertation of Tsinghua University, 2024. WebMildly Conservative Q-Learning for Offline Reinforcement Learning Jiafei Lyu1∗, Xiaoteng Ma 2∗, Xiu Li1†, Zongqing Lu 3† 1Tsinghua Shenzhen International Graduate School, … dictionary in list in python
Jun Zhu
WebAbstract. In recent years, deep reinforcement learning has been developed as one of the basic techniques in machine learning and successfully applied to a wide range of … WebApr 14, 2024 · The existing R-tree building algorithms use either heuristic or greedy strategy to perform node packing and mainly have 2 limitations: (1) They greedily optimize the short-term but not the overall tree costs. (2) They enforce full-packing of each node. These both limit the built tree structure. WebMy current interests are in probabilistic machine learning, adversarial robustness, large-margin learning, Bayesian nonparametrics, deep learning and reinforcement learning. Before joining Tsinghua in 2011, I was a post-doc researcher and project scientist at the Machine Learning Department in Carnegie Mellon University. From 2015 to 2024, I ... city council chief of staff houston tx