Research Focus Area
Reinforcement learning is a machine learning paradigm for sequential decision making in which an agent learns to adapt to changing environmental conditions while performing a task.
Studying methodological advances in the state of the art including hierarchical reinforcement learning, function approximation for reinforcement learning and gradient-descend methods.
Applications of reinforcement learning in a variety of areas including transportation networks, automous robotics, blockchain and fintech.
A video explaining our recent paper is available here >
Research Focus Group Members
Activities include weekly seminars attended by faculty and PhD students across a number of Rossin College of Engineering Arts & Science Departments.