A reinforcement learning agent is tasked with interacting with an unknown environment and learning, through trial and error, a policy that minimizes long-term cost or maximizes long term reward. Problems as diverse as game playing, robotic control, disease management or user experience management fit this model. Research at Duke addresses fundamental questions in reinforcement learning including algorithm design, sample complexity, feature selection and state space representation.
- Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science
- Professor of Computer Science
- Assistant Professor of Biostatistics & Bioinformatics
