In artificial intelligence, "bandits" refers to the multi-armed bandit problem, a mathematical framework for sequential decision-making under uncertainty. The objective is to maximize cumulative reward by choosing at each step among several options ("arms"), each with an unknown reward distribution. The core challenge is balancing exploration (trying new arms to gather information) and exploitation (choosing the apparently best arm). Unlike traditional reinforcement learning, bandit models do not include states or transitions, making them more straightforward for situations where actions depend solely on the present choice.
Use cases and examples
Bandit models are used in adaptive content optimization (dynamic A/B testing), online product recommendation, dynamic ad placement, financial portfolio management, and sensor network optimization. For example, in e-commerce, a bandit system can adaptively display promotions to each user in real time, maximizing conversion probability.
Main software tools, libraries, frameworks
Key libraries for implementing bandit algorithms include Vowpal Wabbit, scikit-learn (for basic models), MABWiser, BanditPylib, and PyBandits. Platforms like Microsoft Azure Personalizer also offer ready-to-use solutions for contextual bandits.
Latest developments, evolutions, and trends
Recent research focuses on contextual bandits that leverage additional information for each draw, adversarial bandits, and integration with deep reinforcement learning. Industrial applications are growing, especially in real-time personalization and automated ad campaign management, with increasing attention paid to algorithmic fairness and robustness in non-stationary environments.