Reinforcement learning applications provide focused models

A common measure of machine intelligence is challenging AI to play complex games against humans. The first AI programs tackled checkers and progressed to beat human players at chess, Go and a wide range of multiplayer games. The thinking behind reinforcement learning (RL) is that if a computer can outwit humans by thinking, planning ahead and predicting human behavior, then the machines have the capacity to learn anything. Now, researchers are still studying how computers learn through iteration and trial and error. One of the simplest goal-driven problems that computers were first tasked with was trying to find the right path through a maze.