Mehul Damani

Hello! I am a second year Ph.D. student at MIT, where I am advised by Jacob Andreas.

My high level research goal is to develop generally capable reinforcement learning (RL) systems that are aligned with human values and goals. I am particularly interested in methods that harness the common-sense knowledge of LLM’s to guide RL agents. I am also interested in studying cooperation in multi-agent systems, which encompasses both pure agent teams as well as human-AI teams. Finally, I am excited by the prospect of applying RL to solve real-world problems such as warehouse automation and traffic signal control.

Previously, I worked with Lerrel Pinto at NYU on developing automatic curriculum learning methods for RL agents. Before that, I was a part of the MARMot Lab at NUS, where I worked with Guillaume Sartoretti on applying multi-agent reinforcement learning to traffic signal control and multi-agent pathfinding.

Selected Publications

  1. NeurIPS
    Mitigating Generative Agent Social Dilemmas
    Yocum, Julian, Christoffersen, Phillip, Damani, Mehul, Svegliato, Justin, Hadfield-Menell, Dylan, and Russell, Stuart
    In NeurIPS 2023 Foundation Models for Decision Making Workshop 2023
  2. TMLR
    Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
    Casper, Stephen, Davies, Xander, Shi, Claudia, Gilbert, Thomas Krendl, Scheurer, Jeremy, Rando, Javier, Freedman, Rachel, Korbak, Tomasz, Lindner, David, Freire, Pedro, and others,
    arXiv preprint arXiv:2307.15217 2023
  3. AAMAS
    SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control
    Goel, Harsh, Zhang, Yifeng, Damani, Mehul, and Sartoretti, Guillaume
    In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems 2023
    PRIMAL2: Pathfinding Via Reinforcement and Imitation Multi-Agent Learning - Lifelong
    Damani, Mehul, Luo, Zhiyao, Wenzel, Emerson, and Sartoretti, Guillaume
    IEEE Robotics and Automation Letters 2021
  5. Springer
    Distributed Reinforcement Learning for Robot Teams: a Review
    Wang, Yutong, Damani, Mehul, Wang, Pamela, Cao, Yuhong, and Sartoretti, Guillaume
    Current Robotics Reports 2022