Deep Reinforcement Learning Advancements and Applications
Recent advances in Deep Reinforcement Learning have captured the imagination of both the AI researchers and the general public. Combining the latest Deep Learning technology with Reinforcement Learning techniques has led to stunning breakthroughs, surpassing human level performances at Atari games and the game of Go. Furthermore, Deep RL is being successfully adopted in a variety of fields such as robotics, control systems, translation, dialogue systems, and others. This talk will explore the intuitions, algorithms, and theories that have led to the recent success of Deep RL. A survey of exciting Deep RL applications and tough challenges ahead will also be discussed.
Charlie obtained his PhD in 2015 in Machine Learning from the University of Toronto, advised by Geoffrey Hinton and Ruslan Salakhutdinov. His thesis focused on various aspects of Deep Learning technology. Charlie also holds a Bachelors in Mechatronics Engineering and Masters in Computer Science from the University of Waterloo. After his PhD, along with Ruslan Salakhutdinov and Nitish Srivastava, Charlie co-founded a startup focused on the application of Deep Learning based vision algorithms. Currently, Charlie is a research scientist at Apple Inc. Charlie's research interests include Deep Learning, Vision, Neuroscience and Robotics. He is one of the few competitors to have reached the #1 ranking on Kaggle.com, a widely popular machine learning competition platform. Charlie is also a Canadian national chess master.