Reinforcement Learning is not a new concept, but has been developed and matured over 70 years of academic rigour. Fundamentally, Reinforcement Learning is a method of machine learning by which an algorithm can make decisions and take actions within a given environment, and learns what appropriate decisions to make through repeated trial-and-error actions. The academic discourse for Reinforcement Learning pursued three concurrent ‘threads’ of research (trial and error, optimal control, and temporal difference), before being united in the research in the 1990’s. Reinforcement Learning was then able to proceed to mastering the playing of Chess, and of Go, and of countless electronic games. The modern applications of Reinforcement Learning are enabling businesses to optimise, control, and monitor their respective processes, to a phenomenal level of accuracy and finesse. As a result, the future of Reinforcement Learning is both exciting and fascinating, as the research aims to improve the algorithm’s interpretability, accountability and trustworthiness.
A Review of the Historic, Modern, and Future Applications of this Special Form of Machine Learning
"The future of Reinforcement Learning will soon infiltrate our everyday lives in many, many different ways; but not before a few fundamental issues are rectified with its interpretability, accountability, and trustworthiness." Chris Mahoney dives into RL https://t.co/THQY8AghkO— Towards Data Science (@TDataScience) June 14, 2021