Real-World Reinforcement Learning: Challenges and Opportunities
Applications of Artificial Intelligence (AI) in the real world are most commonly based on Supervised Learning — a framework in which systems learn to describe objects. A much more powerful type of AI is Reinforcement Learning (RL). In RL, systems learn to not only describe objects, but also take actions in a dynamic environment so as to achieve some long-term goal. The general definition of RL is so far reaching that one may say it encompasses the essence of intelligence. Unfortunately, to date, RL is largely an academic domain, with few applications in the real world. The reason lies in a series of challenges that must be overcome in order to turn RL into a reality. In this talk I will explain what RL is, discuss its potential to transform various industries, review challenges associated with its application, and describe how, in some cases, these challenges can be overcome, resulting in AI systems that not long ago were considered imaginary.