Ben Lau is a quantitative researcher in a macro hedge fund in Hong Kong and he looks to apply mathematical models and signal processing techniques to study the financial market. Prior joining the financial industry, he specialized in using his mathematical modelling skills to discover the mysteries of the universe whilst working at Stanford Linear Accelerator Centre, a national accelerator laboratory where he studied the asymmetry between matter and antimatter by analysing tens of billions of collision events created by the particle accelerators. Ben was awarded his Ph.D. in Particle Physics from Princeton University and his undergraduate degree (with First Class Honours) at the Chinese University of Hong Kong.
Deep Reinforcement Learning: Developing a robotic car with the ability to form long term driving strategies is the key for enabling fully autonomous driving in the future. Reinforcement learning has been considered a strong AI paradigm which can be used to teach machines through interaction with the environment and by learning from their mistakes. In this talk, we will discuss how to apply deep reinforcement learning technique to train a self-driving car under an open source racing car simulator called TORCS. I am going to share how this is implemented and will discuss various challenges in this project.