I am a first-year Ph.D. student in Computer Science at the University of Maryland, College Park, where I am fortunate to be advised by Prof. Furong Huang and Prof. Hal Daumé III. Before that, I obtained my Bachelor’s degree in Computer Science and Mathematics also from the University of Maryland, College Park. My research spans a variety of topics in reinforcement learning (RL), including model-based RL, transfer RL, adversarial RL, etc. My long-term goal is to make RL agents more robust and efficient.
In model-based RL, my work provided a novel insight into probabilistic environment model ensemble, which is commonly used in model-based RL algorithm. Based on the insight, we can substitute the ensemble with a single model and Lipschitz regularized value function to make the learning algorithm much more computationally efficient. For transfer-RL, I have worked on transferring domain knowledge under the drastic change of observation spaces (e.g., from vector-based observation to image-based observation). For adversarial RL, I have worked on observation attacks for the deep RL policy, as well as designing efficient algorithms to improve the agent’s robustness under attack. In addition, I have also worked on communication attacks in multi-agent reinforcement learning and developed a certifiable defense mechanism.