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  • PhD Defense- Tiancheng Jin

    Mon, Apr 22, 2024 @ 04:00 PM - 05:30 PM

    Thomas Lord Department of Computer Science

    Student Activity

    PhD Defense- Tiancheng Jin
    Title: Robust and Adaptive Online Reinforcement Learning 
    Committee: Haipeng Luo (Chair), Rahul Jain, Vatsal Sharron
    Abstract: Reinforcement learning (RL) is a machine learning (ML) technique on learning to make optimal sequential decisions via interactions with an environment. In recent years, RL achieved great success in many artificial intelligence tasks, and has been widely regarded as one of the keys towards Artificial General Intelligence (AGI). However, most RL models are trained on simulators, and suffer from the reality gap: a mismatch between simulated and real-world performance. Moreover, recent work has shown that RL models are especially vulnerable to adversarial attacks. This motivates the research on improving the robustness of RL, that is, the ability of ensuring worst-case guarantees.

    On the other hand, it is not favorable to be too conservative/pessimistic and sacrifice too much performance while the environment is not difficult to deal with.In other words, adaptivity --- the capability of automatically adapting to the maliciousness of the environment, is especially desirable to RL algorithms: they should not only target worst-case guarantee, but also pursue instance optimality and achieve better performance against benign environments.
    In this thesis, we focus on designing practical, robust and adaptive reinforcement algorithms.

    Specifically, we take inspiration from the online learning literature, and consider interacting with a sequence of Markov Decision Processes (MDPs), which captures the nature of changing environment. We hope that the techniques and insight developed in this thesis could shed light on improving existing deep RL algorithms for future applications.

    Location: Kaprielian Hall (KAP) - 141

    Audiences: Everyone Is Invited

    Contact: Tiancheng Jin

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