Thursday, Jan 13 2022 - 10:00 am (GMT + 7)

Provable Offline Reinforcement Learning: Neural Function Approximation, Randomization, and Sample Complexity

About the speaker

Thanh Nguyen-Tang is a postdoctoral research fellow in the Department of Computer Science at Johns Hopkins University. His research focuses on algorithmic and theoretical foundations of modern machine learning, aiming to build data-efficient, deployment-efficient, and robust AI systems. He has published his works in various top-tier conferences in machine learning including NeurIPS, ICLR, AISTATS, and AAAI. Thanh finished his Ph.D. in Computer Science at the Applied AI Institute at Deakin University, Australia.


In this talk, I will share some of my recent results on offline reinforcement learning (RL), an RL paradigm for domains where exploration is prohibitively expensive or even implausible, but a fixed dataset of previous experiences is available a priori. Specifically, I will focus on discussing how deep neural networks (trained by (stochastic) gradient descents) and randomization lead to a computationally efficient algorithm that has a strong theoretical guarantee for generalization across large state spaces under mild assumptions of distributional shifts while obtaining a favorable empirical performance. I will conclude with a discussion on future directions to make RL more data-efficient, deployment-efficient, and robust.

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