The Dynamic Optimization and Reinforcement Learning Lab builds principled, data-driven methods for intelligent systems that learn, plan, and adapt autonomously.
Embedded within the University of Toronto's interdisciplinary research ecosystem, the Dynamic Optimization and Reinforcement Learning (DORL) Lab publishes in leading academic venues with contributions that push the boundaries of intelligent systems that learn, plan, and adapt autonomously. Our work spans theory and practice through innovations in deep learning, reinforcement learning, robotics, and operations research, with the goal of developing efficient, generalizable, and trustworthy intelligent systems.
See what we're working on