Jonathan How

Jonathan P. 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.

Publications

• 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, Macau, China

Videos