SYNOPSIS

Reinforcement learning (RL) is an artificial intelligence approach that enables agents (or decision makers) to learn and take optimal actions in a dynamic and unpredictable operating environment. Compared to other artificial intelligence approaches, including supervised and unsupervised learning, RL has a distinguishing aspect in which it learns through interaction with the environment. Based on the rewards (or penalties) received from the environment, a decision maker evaluates the appropriateness of its selected action under a particular environment. RL has drawn wide interest in exploring and exploiting its application in solving a diverse range of problems and enhancing next-generation technologies. This talk covers the fundamentals, algorithms, and applications of RL. Ultimately, it guides participants in exploring the use of RL to solve problems and issues at hand.


BIODATA OF SPEAKER

Prof. Dr Yau Kok Lim received the B.Eng. degree (Hons.) in electrical and electronics engineering from Universiti Teknologi PETRONAS, Malaysia, in 2005, the M.Sc. degree in electrical engineering from the National University of Singapore, in 2007, and the Ph.D. degree in network engineering from the Victoria University of Wellington, New Zealand, in 2010. His research area is applied artificial intelligence, and has offered consultancies to companies in microelectronics and intelligent transportation systems. He was a recipient of the 2007 Professional Engineer Board of Singapore Gold Medal for being the best graduate of the M.Sc. degree. He serves as the Lead Organising Chair for AICS'22, Vice General Co-Chair for ICOIN’18, the Co-Chair for IET ICFCNA’14, and the Co-Chair (Organizing Committee) for IET ICWCA’12. He serves as an Editor for the KSII Transactions on Internet and Information Systems, and an Associate Editor for IEEE Access. He is also the Vice-Chair of IEEE SIG on Big Data with Computational Intelligence.