Thompson Sampling, also known as Bayesian Bandit or Posterior Sampling, is an algorithm used primarily in the context of multi-armed bandit problems and reinforcement learning. It is designed to address the fundamental challenge of balancing exploration and exploitation. Exploration involves trying out new actions to gather more information about their potential rewards, while exploitation focuses