Thompson Sampling
Thompson Sampling is a probabilistic algorithm used in decision-making problems where the goal is to maximize the cumulative reward. It is commonly used in the field of reinforcement learning and multi-armed bandit problems. The algorithm maintains a probability distribution over the possible actions and selects an action based on the probability of it being the optimal action. After each action, the probability distribution is updated based on the observed reward. The algorithm balances exploration and exploitation by randomly selecting actions based on the probability distribution, which allows it to discover new actions that may have a higher reward. Thompson Sampling has been shown to have strong empirical performance in a variety of applications, including online advertising, recommendation systems, and clinical trials.
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