Epsilon-greedy
Epsilon-greedy is a common algorithm used in reinforcement learning and decision-making processes. It is a simple approach that balances exploration and exploitation by selecting the best known option with probability 1-epsilon and a random option with probability epsilon. The epsilon value is typically small, such as 0.1 or 0.01, to ensure that the algorithm mostly selects the best known option. However, the occasional random selection allows for exploration of other options and can prevent the algorithm from getting stuck in a suboptimal solution. Epsilon-greedy is often used in multi-armed bandit problems, where the goal is to maximize the total reward over a series of choices. It is also used in other applications such as recommendation systems and online advertising.
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