PhD Defense: Towards Robust and Adaptive Real-World Reinforcement Learning
Yanchao Sun
Friday, April 14, 2023, 12:00-1:30 pm 
Abstract
The past decade has witnessed a rapid development of reinforcement learning (RL) techniques. However, there is still a gap between employing RL in simulators and applying RL models to challenging and diverse real-world systems. On the one hand, existing RL approaches have been shown to be fragile under perturbations in the environment, making it risky to deploy RL models in real-world applications where unexpected noise and interference exist. On the other hand, most RL methods focus on learning a policy in a fixed environment, and need to re-train a policy if the environment gets changed. For real-world environments whose specifications and dynamics can be ever-changing, these methods become less practical as they require a large amount of data and computations to adapt to a changed environment.
This talk focuses on the above two challenges, and introduces a series of solutions to improve the robustness and adaptability of RL methods. For robustness, the proposed approaches explore the vulnerability of RL agents in multiple scenarios, and achieve state-of-the-art performance on robustifying RL policies. For adaptability, the proposed transfer learning and pretraining frameworks address challenging multi-task learning problems where tasks specifications can be drastically different.
This talk focuses on the above two challenges, and introduces a series of solutions to improve the robustness and adaptability of RL methods. For robustness, the proposed approaches explore the vulnerability of RL agents in multiple scenarios, and achieve state-of-the-art performance on robustifying RL policies. For adaptability, the proposed transfer learning and pretraining frameworks address challenging multi-task learning problems where tasks specifications can be drastically different.
Examining Committee
Bio
Yanchao Sun is a 5th-year Ph.D. student at the University of Maryland, College Park, advised by Dr. Furong Huang. Her research interests lie in reinforcement learning, adversarial learning, representation learning, transfer learning, and their intersections.
This talk is organized by Tom Hurst