A core topic in Machine Learning is that of sequential decision-making. This is the task of deciding, from experience, the sequence of actions to perform in an uncertain environment in order to achieve some goals. Sequential decision-making tasks cover a wide range of possible applications with the potential to impact many domains, such as finance (intelligent algorithmic trading), robotics, healthcare, self-driving cars, and many more. Inspired by behavioural psychology, Deep Reinforcement Learning (RL) proposes a formal framework to this problem. Deep RL has also started to receive a lot of attention since the January of 2016, when a team of researchers from Google built a Deep RL based AI that beat the reigning world champion of the board game Go. So what is Deep RL? Let’s explore!
If you have not heard of Deep Reinforcement Learning (RL) before, here’s a short video clip of an intelligent agent created by Google’s AI company, DeepMind, that has learned (using Deep RL) how to walk, run, jump, and climb without any prior guidance. The result is as impressive as it is goofy!