Reinforcement Learning

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Reinforcement Learning

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.

Recent Research

"Multi-Agent Image Classification via Reinforcement Learning"
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.