Sponsor
Computer Science
Co-Sponsor(s)
Electrical and Computer Engineering (ECE); Mathematics; Pratt School of Engineering; Statistical Science
Towards a Foundation for Reinforcement Learning
Wednesday, March 23,
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Speaker(s): Ruosong Wang
In recent years, reinforcement learning algorithms have achieved strong empirical success on a wide variety of real-world problems. However, these algorithms usually require a huge number of samples even just for solving simple tasks. It is unclear if there are fundamental statistical limits on such methods, or such sample complexity burden can be alleviated by a better algorithm. In this talk, I will give an overview of my research efforts towards bridging the gap between the theory and the practice of reinforcement learning.
Speaker Ruosong Wang is currently a Ph.D. student at Carnegie Mellon University, advised by Prof. Ruslan Salakhutdinov. He did his undergraduate study at Yao Class, Tsinghua University. He has also spent time at Simons Institute and Microsoft Research. He is broadly interested in the theory and the practice of modern machine learning paradigms with a focus on reinforcement learning.
Speaker Ruosong Wang is currently a Ph.D. student at Carnegie Mellon University, advised by Prof. Ruslan Salakhutdinov. He did his undergraduate study at Yao Class, Tsinghua University. He has also spent time at Simons Institute and Microsoft Research. He is broadly interested in the theory and the practice of modern machine learning paradigms with a focus on reinforcement learning.
