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Abstract:

Reinforcement learning (RL) has achieved great empirical success in many real-world problems in the last few years. However, many RL algorithms are inefficient due to their data-hungry nature. Whether there exists a universal way to improve the efficiency of existing RL algorithms remains an open question. 

In this talk, I will give a selective overview of my research, which suggests that efficient (and optimal) RL can be built through the lens of uncertainties. I will show that uncertainties can not only guide the agent to make better decisions, but also have the ability to accelerate the learning of the optimal policy over a finite number of data samples collected from the unknown environment. By utilizing the proposed uncertainty-based framework, I design computationally efficient and statistically optimal RL algorithms under various settings, which improve existing baseline algorithms from both theoretical and empirical aspects. At the end of this talk, I will briefly discuss several of my additional works, and my future research plan for designing next-generation RL algorithms.

Bio:

Dongruo Zhou is a final-year PhD student in the Department of Computer Science at UCLA, advised by Prof. Quanquan Gu. His research is broadly on the foundation of machine learning, with a particular focus on reinforcement learning and stochastic optimization. He aims to provide a theoretical understanding of machine learning methods, as well as to develop new machine learning algorithms with better performance. He is a recipient of the UCLA dissertation year fellowship.

Zoom link for the talk: https://www.cs.wm.edu/zoom

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