The "multiarmed bandit" problem teaches us how to balance exploring new options and exploiting the best ones from "summary" of Algorithms to Live By by Brian Christian,Tom Griffiths
Imagine standing in front of a row of slot machines in a casino. Each machine has a different payout rate, and you have limited time and money to figure out which one is the best. This classic scenario is known as the "multiarmed bandit" problem, where you need to balance between exploring new options (trying different machines) and exploiting the best one (sticking to the machine with the highest payout). The essence of the multiarmed bandit problem lies in the tension between exploration and exploitation. If you only focus on exploration, trying out every machine without exploiting the best one, you may miss out on maximizing your gains. On the other hand, if you only exploit, sticking to one machine without exploring other options, you may be st...
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