Multi-armed bandit

In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or by allocating resources to the choice. This is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma. The name comes from imagining a gambler at a row of slot machines (sometimes known as "one-armed bandits"), who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. The multi-armed bandit problem also falls into the broad category of stochastic scheduling.

Multi-armed bandit - Wikipedia Jump to content From Wikipedia, the free encyclopedia Resource problem in machine learning A row of slot machines in Las Vegas In probability theory and machine learning , the multi-armed bandit problem (sometimes called the K - [ 1 ] or N -armed bandit problem [ 2 ] ) is named from imagining a gambler at a row of slot machines (sometimes known as " one-armed bandits "), who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. [ 3 ] M

related reading

• The Multi-Armed Bandit Problem and Its Solutions | Lil'Loglilianweng.github.io
• The Bayes Banditfrancesco215.github.io
• Corruption-tolerant bandit learning | Machine Learning | Springer Nature Linklink.springer.com
• Contextual Bandits and the Exp4 Algorithm – Bandit Algorithmsbanditalgs.com
• Epsilon-Greedy Algorithm in Reinforcement Learning - GeeksforGeeksgeeksforgeeks.org
• [2305.18784] Collaborative Multi-Agent Heterogeneous Multi-Armed Banditsarxiv.org
• Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancementarxiv.org
• Gittins index - Wikipediaen.wikipedia.org
• Evolution as Backstop for Reinforcement Learning · Gwern.netgwern.net
• An Overview of Contextual Bandits | Towards Data Sciencetowardsdatascience.com
• A (Long) Peek into Reinforcement Learning | Lil'Loglilianweng.github.io
• Bayesian Bandits - optimizing click throughs with statistics - Chris Stucchiochrisstucchio.com