Exploration-Exploitation Trade-offs

Exploration-Exploitation Trade-offs refer to the dilemma faced by decision-makers when choosing between exploring new options and exploiting known options. In Data Science, this trade-off is often encountered in the context of Reinforcement Learning, where an agent must decide whether to continue exploring new actions or to exploit the actions that have already been determined to be optimal. The optimal balance between exploration and exploitation depends on the specific problem and the available resources. Too much exploration can lead to wasted resources, while too much exploitation can lead to suboptimal solutions. Various algorithms have been developed to address this trade-off, such as epsilon-greedy, Upper Confidence Bound (UCB), and Thompson Sampling.
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