Reinforcement learning (RL), a sub-discipline of machine learning, has been gaining academic and media notoriety after hyped marketing “reveals” of agents playing various games. But these hide the fact that RL is immensely useful in may practical, industrial situations where hand-coding strategies or policies would be impractical or sub-optimal.
Following the theme of my new book (https://rl-book.com), I present a rebuttal to the hyperbole by analysing five different industrial case studies from a variety of sectors. You will learn where RL can be applied, how to spot challenges that fit inside the RL paradigm, and what pitfalls to watch out for. You will learn that RL is more than an bot in a game; it is the next frontier in applied artificial intelligence.
I avoid using jargon to make this talk acceptable for a wider audience. I do expect that you have limited exposure to data science/machine learning in general.