I created this series to make that steep learning curve far less daunting for you. My goal is to trace the evolution of RL chronologically—from its roots in early psychology and physical mechanical machines to digital binary systems and modern mathematical breakthroughs. By breaking down complex concepts with clear visual guides, graphics, and real-world analogies, I hope to demystify RL and give back to the community that inspired me. Let’s dive in!
Blog 1: The Psychological Seeds (1898–1949)
This entry explores the Law of Effect (1911), where Edward Thorndike established that actions followed by satisfaction are strengthened. It also covers Ivan Pavlov’s formal definition of reinforcement (1927) and Donald Hebb’s 1949 hypothesis that "neurons that fire together, wire together," laying the groundwork for neural learning.
Blog 2: Cybernetics and Early Machines (1948–1954)
This blog details the first computational investigations, including Alan Turing’s "pleasure-pain system" (1948) and Marvin Minsky’s construction of SNARCs (1954), the first analog neural-network reinforcement calculators. It also highlights Claude Shannon’s 1952 demonstration of "Theseus," a maze-running mouse that used trial and error to "remember" paths.
