Reinforcement learning (RL) addresses the problem of how an autonomous active agent can learn to approximate an optimal behavioral strategy while interacting with its environment. It has been widely applied in various areas including artificial intelligence, control engineering, operations research and robotics. In this talk, we will introduce several improved reinforcement learning algorithms which were developed by my collaborators and myself. These algorithms include incremental reinforcement learning, quantum reinforcement learning, quantum-inspired deep reinforcement learning. We will also demonstrate several applications of these improved reinforcement learning algorithms to robotics, games and quantum control.

Biography:

A/Prof Daoyi Dong is currently a Scientia Associate Professor at the University of New South Wales, Canberra, Australia. His research interests include quantum control and machine learning. He serves as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, and Technical Editor of IEEE/ASME Transactions on Mechatronics. He has also served as General Chair or Program Chair for several international conferences, and is currently Associate Vice-President and a Member-at-Large of Board of Governors, IEEE Systems, Man and Cybernetics Society.