Bayesian Reinforcement Learning with Limited Cognitive Load
Dilip Arumugam*, Mark K. Ho*, Noah D. Goodman, Benjamin Van Roy.
Open Mind: Discoveries in Cognitive Science -- to appear, 2024.
I am a final-year Ph.D. candidate in the Stanford University Computer Science Department, advised by Benjamin Van Roy. My research is broadly focused on reinforcement learning. In the past, I have completed internships at Microsoft Research Cambridge, Mila, Microsoft Research Redmond, and DeepMind.
I am on the academic & industry job markets for the 2023-2024 cycle.
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 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.
I'm primarily interested in the area of reinforcement learning with the goal of building sequential decision-making agents that learn as efficiently and as remarkably as people do. My work employs a variety of techniques for developing principled approaches to address the core challenges of sample-efficient reinforcement learning: generalization, exploration, and credit assignment. Lately, I've been studying information theory as a collection of tools that facilitate rigorous analysis while also remaining amenable to the design of practical, scalable agents.
For my CV, please click here and, for a complete list of papers, please check Google Scholar, DBLP, or Semantic Scholar (depending on what you're after, one of these may be more reliable than the others).
Dilip Arumugam*, Mark K. Ho*, Noah D. Goodman, Benjamin Van Roy.
Open Mind: Discoveries in Cognitive Science -- to appear, 2024.
Ben Prystawski, Dilip Arumugam, Noah D. Goodman.
Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci), 2023.
Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy.
NeurIPS Workshop on Information-Theoretic Principles in Cognitive Systems, 2022.
Dilip Arumugam & Benjamin Van Roy.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
ICML Workshop on Decision Awareness in Reinforcement Learning, 2022.
Early version: Multi-disciplinary Conference on Reinforcement Learning & Decision Making (RLDM), 2022.
Dilip Arumugam & Satinder Singh.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
Early version: NeurIPS Workshop on Ecological Theory of Reinforcement Learning, 2021.
Dilip Arumugam & Benjamin Van Roy.
Advances in Neural Information Processing Systems (NeurIPS), 2021.
David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L.S. Wong.
ICML Workshop on Reinforcement Learning Theory, 2021.
Dilip Arumugam & Benjamin Van Roy.
International Conference on Machine Learning (ICML), 2021.
Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon.
NeurIPS Workshop on Biological and Artificial Reinforcement Learning, 2020.
Dilip Arumugam & Benjamin Van Roy.
NeurIPS Workshop on Biological and Artificial Reinforcement Learning, 2020.
David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
Multi-disciplinary Conference on Reinforcement Learning & Decision Making (RLDM), 2019.
Early version: ICLR Workshop on Structures and Priors in Reinforcement Learning, 2019.
Pierre-Luc Bacon, Dilip Arumugam, Emma Brunskill.
Multi-disciplinary Conference on Reinforcement Learning & Decision Making (RLDM), 2019.
David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L.S. Wong.
Association for the Advancement of Artificial Intelligence (AAAI) Conference, 2019.
Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman.
Preprint, 2018.
Dilip Arumugam*, Siddharth Karamcheti*, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L.S. Wong, Stefanie Tellex Autonomous Robots (AuRo), 2018.
David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman.
International Conference on Machine Learning (ICML), 2018.
Early version: NIPS Workshop on Hierarchical Reinforcement Learning, 2017.
Nakul Gopalan*, Dilip Arumugam*, Lawson L.S. Wong, Stefanie Tellex.
Robotics: Science and Systems, 2018.
Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman.
Preprint, 2017.
Dilip Arumugam*, Siddharth Karamcheti*, Nakul Gopalan, Lawson L.S. Wong, Stefanie Tellex.
Robotics: Science and Systems, 2017.
James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang.
Robotics: Science and Systems, 2015.
I've had the privilege of both learning from and researching with an amazing group of people: