DEV Community Series Traces Reinforcement Learning From 1911 Psychology to Modern AI

A developer on DEV Community has published a multi-part educational series charting the chronological history of reinforcement learning (RL), from early psychological theories to contemporary algorithms. The series begins with foundational ideas such as Thorndike's Law of Effect (1911), Pavlov's reinforcement concept (1927), and Hebb's neural learning hypothesis (1949). It progresses through landmark computational milestones including Turing's pleasure-pain system (1948), Bellman's Dynamic Programming (1957), and Watkins's Q-Learning (1989). Later entries cover breakthroughs like Tesauro's TD-Gammon (1992) and the Policy Gradient Theorem (2000), which advanced modern RL theory. The series uses visual guides and real-world analogies to make complex RL concepts accessible to a broad audience.