MABprogram

MABprogram is a software application designed to facilitate the management of Multi-Armed Bandit (MAB) problems. MAB problems are a class of reinforcement learning problems where an agent must choose between multiple options, each with an unknown reward distribution. The goal is to maximize the cumulative reward over a series of trials by balancing exploration (trying new options) and exploitation (choosing the best-known option).

MABprogram provides a user-friendly interface for defining and solving MAB problems. Users can specify the number of arms (options), the reward distributions for each arm, and the number of trials. The software supports various MAB algorithms, including epsilon-greedy, upper confidence bound (UCB), and Thompson sampling. These algorithms differ in their approach to balancing exploration and exploitation.

The application is particularly useful for researchers and practitioners in fields such as machine learning, operations research, and economics. It allows users to experiment with different MAB algorithms and compare their performance under various conditions. MABprogram also includes visualization tools to help users understand the results and the behavior of the algorithms.

In addition to its core functionality, MABprogram offers advanced features such as custom reward distributions, adaptive algorithms, and batch processing. These features make it a versatile tool for both educational purposes and real-world applications. The software is available for multiple platforms, including Windows, macOS, and Linux, ensuring broad accessibility for users.