Bandit slot machines refer to a classic problem in probability theory and machine learning known as the multi-armed bandit problem. This framework models a gambler facing multiple slot machines （bandits）, each with an unknown probability of providing a reward. The central challenge is to balance exploration （trying different machines to gather information） and exploitation （playing the machine that seems best based on current knowledge） to maximize the total reward earned over a sequence of plays.Michigan Slot Machines: A Guide to the Best Games and Casinos 
The term \“bandit\“ originates from the nickname for slot machines, \“one-armed bandits,\“ due to their single lever. In the multi-armed version, the gambler must decide which \“arm\“ to pull at each turn. Algorithms designed to solve this problem, such as the Epsilon-Greedy algorithm or Upper Confidence Bound （UCB）, are fundamental to areas like reinforcement learning, clinical trials, and website banner ad selection, where optimal decision-making under uncertainty is required. |
