I am a PhD student at MIT advised by Prof. Jonathan P. How. I am fortunate to have Prof. Jakob N. Foerster at Oxford and Prof. Pulkit Agrawal at MIT as my PhD committee members.

My research focuses on the fields of reinforcement learning and robotics. Specifically, I am interested in multiagent reinforcement learning (MARL) for learning to interact with other simultaneously learning agents. I am also interested in other related machine learning topics, such as meta-learning for enabling a robot to adapt fast to unseen situations, hierarchical learning for solving the delayed credit assignment issue, and safe learning for learning a policy without violating safety constraints.

Previously, I received my B.S. (summa cum laude) at Cornell, working on computer vision. Before MIT, I received guidance from wonderful advisors: Prof. Sebastian Scherer at CMU-RI, Prof. Matthew R. Walter at TTIC, and Prof. Tsuhan Chen at Cornell.

🤖 Please refer to my CV for more details about my education and experiences.

Selected News

Publication

Reinforcement Learning

  • Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How. Influencing Long-Term Behavior in Multiagent Reinforcement Learning. In Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code soon] [Video]
  • Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How. ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation. In International Conference on Robotics and Automation (ICRA), 2022. [Paper]
  • Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How. Context-Specific Representation Abstraction for Deep Option Learning. In Association for the Advancement of Artificial Intelligence (AAAI), 2022. [Paper] [Code] [Video]
  • Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How. A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. In International Conference on Machine Learning (ICML), 2021. [Paper] [Code] [Video]
  • Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How. FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimizer. In International Conference on Robotics and Automation (ICRA), 2021. [Paper]
  • Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How. Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2020. [Paper] [WIRED News]
  • Samir Wadhwania, Dong-Ki Kim, Shayegan Omidshafiei, Jonathan P. How. Policy Distillation and Value Matching in Multiagent Reinforcement Learning. In International Conference on Intelligent Robots and Systems (IROS), 2019. [Paper] [Video]
  • Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How. Learning to Teach in Cooperative Multiagent Reinforcement Learning. In Association for the Advancement of Artificial Intelligence (AAAI), 2019. [Paper] [MIT News] [Outstanding Student Honorable Paper] 🎉
  • Shayegan Omidshafiei*, Dong-Ki Kim*, Jason Pazis, Jonathan P. How. Crossmodal Attentive Skill Learner. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018 and Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2020. [Conference Paper] [Journal Paper] [Code] [Video]

Computer Vision & Robotics

  • Lena M. Downes, Dong-Ki Kim, Ted J. Steiner, Jonathan P. How. City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent. In International Conference on Intelligent Robots and Systems (IROS), 2022. [Paper] [Video]
  • Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How. Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC. In International Conference on Robotics and Automation (ICRA), 2022. [Paper] [Video]
  • Dong-Ki Kim, Matthew R. Walter. Satellite Image-based Localization via Learned Embeddings. In International Conference on Robotics and Automation (ICRA), 2017. [Paper] [Video] [NVIDIA News]
  • Dong-Ki Kim, Daniel Maturana, Masashi Uenoyama, Sebastian Scherer. Season-Invariant Semantic Segmentation with A Deep Multimodal Network. In Field and Service Robotics (FSR), 2017. [Paper]
  • Hang Chu, Dong-Ki Kim, Tsuhan Chen. You Are Here: Mimicking the Human Thinking Process in Reading Floor-Plans. In International Conference on Computer Vision (ICCV), 2015. [Paper] [Video]
  • Dong-Ki Kim, Tsuhan Chen. Deep Neural Network for Real-Time Autonomous Indoor Navigation. Technical Report, 2015. [Paper] [Video]