Part 4/14:
Historically, the inception of reinforcement learning in machines dates back to the 1960s when Donald Michie showcased the first example using matchboxes and colored beads to play Tic-Tac-Toe. His innovative method reinforced winning strategies based on collected experiences rather than direct programming. However, his method had limitations as it demanded human selections for every possible situation, indicating that machines required a more autonomous ability to recognize patterns independently. This notion leads us to abstraction—the machine's capability to overlook trivial differences and focus on fundamental similarities.