The famous MENACE matchbox computer for playing tic-tac-toe, invented by Donald Mitchie, is an early example of a reinforcement learning algorithm. Here is a description:
...an interesting machine for playing tic-tac-toe. It was made entirely out of matchboxes, each one of which had a tic tac toe position on the top. Inside was a collection of colored beads. Each color specified a possible legal move for the position on top. The idea was that you’d play a game by drawing these beads from the appropriate box, and making the appropriate move. At the end of the game, you’d remove the bead from the last box that sent you along the losing path. Eventually, all the losing moves get removed, and the machine plays perfect tic-tac-toe. Gardner showed how this same idea could be used to create a matchbox computer to play hexapawn, a simple game played with six pawns on a 3×3 board.
My question is: what kind of algorithm is this, in modern parlance? Is it policy iteration? Q-learning? Or sooething else? I am a bit new to the subject and can't figure it out for sure.