Dilip Arumugam

I'm a second-year Ph.D. student in the Stanford University Computer Science Department, where I am advised by Benjamin Van Roy. My research is broadly focused on reinforcement learning.

Previously, I completed my Bachelor's and Master's degrees in the Brown University Computer Science Department. My time at Brown centered around work in reinforcement learning under my advisor, Michael L. Littman. In parallel, I was a member of the Humans to Robots Laboratory where I worked with Stefanie Tellex on natural language understanding for robots. I was also a member of the Brown Laboratory for Linguistic Information Processing run by Eugene Charniak.

Research Interests

My core area of research is reinforcement learning with the goal of building and understanding sequential decision-making agents that learn as efficiently and as remarkably as people do.

To this end, while the successes of reinforcement learning in the single-task setting are incredibly exciting, I am most drawn to problems that stand in the way of efficient multi-task and lifelong learning. Such problems include (but are by no means limited to) efficient exploration, hierarchical reinforcement learning, and transfer learning. I believe that a key ingredient lying at the heart of overcoming these challenges is abstraction whereby an agent's ability to acquire and maintain representations of latent task and environment structure is crucial to robust decision making.

Other areas of active interest include reinforcement learning with natural language, model-based reinforcement learning, information-theoretic decision making, and learning reward functions.

Collaborators

I've had the privilege of both learning from and researching with an amazing group of people: