Multi-armed bandit - Wikipedia
More generally, it is a problem in which a decision maker iteratively selects one of multiple fixed choices (i.e., arms or actions) when the properties of each choice are only partially known at …
At each node in the tree, a bandit algorithm is used to select the child based on the series of rewards observed through that node so far. The resulting algorithm can be analysed …
Multi-armed Bandit Problem in Reinforcement Learning
Oct 27, 2025 · The implementation provided demonstrates the Epsilon-Greedy algorithm, which is a common strategy for solving the Multi-Armed Bandit (MAB) problem. The code aims to …
Introduction to Multi-Armed Bandits | TensorFlow Agents
Sep 26, 2023 · Multi-Armed Bandit (MAB) is a Machine Learning framework in which an agent has to select actions (arms) in order to maximize its cumulative reward in the long term.
In this lecture, we begin with a general overview of the two problem structures with which this course is concerned: (1) multi-armed bandits and (2) reinforcement learning. From this …
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered …
Bandit Algorithms - Cambridge University Press & Assessment
8 - The Upper Confidence Bound Algorithm: Asymptotic Optimality pp 97-102 Get access Export citation
Bandit Algorithms: A Comprehensive Guide
Jun 16, 2025 · Explore the mathematical foundations and applications of bandit algorithms in computer science, including their role in decision-making and optimization.
Implementing Multi-Armed Bandits: A Beginner’s Hands-on Guide ...
May 4, 2025 · Multi-armed bandit algorithms present basic logic that operates through a clear example comparing them to slot machines. They remain understandable to people who no …
For an in-depth treatment, we suggest the recent book Bandit algorithms by Lattimore and Szepesvari (2018). See also this tutorial or this blog.