Reimagining Reinforcement Learning – Upside Down

Summary: For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications. Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train. Ever since Reinforcement Learning (RL) was recognized as a legitimate third style of machine learning alongside supervised and unsupervised learning we've been waiting for that killer app to prove its value. Yes RL has had some press-worthy wins in game play (Alpha Go), self-driving cars (not here yet), drone control, and even dialogue systems like personal assistants but the big breakthrough isn't here yet. RL ought to be our go-to solution for any problem requiring sequential decisions and these individual successes might make you think that RL is ready for prime time but the reality is that it's not.