Jonathan How

Professor of Aeronautics and Astronautics

Jonathan How is the Richard C. Maclaurin Professor of Aeronautics and Astronautics in MIT’s Department of Aeronautics and Astronautics. His research interests include navigation and control; design and implementation of distributed robust planning algorithms to coordinate multiple autonomous vehicles in dynamic uncertain environments; adaptive flight control to enable autonomous agile flight and aerobatics and experimental and theoretical robust control. He is a fellow of the American Institute of Aeronautics and Astronautics (AIAA) and the Institute of Electrical and Electronics Engineers, and has received the 2020 AIAA Intelligent Systems Award. He earned a BS from the University of Toronto, and an MS and PhD from MIT.


  • Hoseini, S., Liu, H. H. T., Everett, M., Ruiter, A. de, and How, J. P. (2020). Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning. IEEE Robotics and Automation Letters (RA-L), (to appear)
  • Dong Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan How (2020). Learning Hierarchical Teaching Policies for Cooperative Agents. International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)
  • Shayegan Omidshafiei, Dong Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris Amato, Murray Campbell, Jonathan How. (2019). Learning to Teach in Cooperative Multiagent Reinforcement Learning. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
  • Wadhwania, S., Kim, D.-K., Omidshafiei, S., and How, J. P. (2019). Policy Distillation and Value Matching in Multiagent Reinforcement Learning. IEEE/RSJ International Conference on Intelligent Robots and Systems,┬áMacau, China