The exploration-exploitation trade-off is a fundamental concept in decision-making processes, particularly within the context of reinforcement learning, where an agent must choose between exploring new strategies or exploiting known strategies that yield the highest rewards. This balance is crucial for optimizing learning and performance in environments that require continual adaptation, such as IoT systems. In the realm of IoT, it impacts how devices interact with their environment to learn and improve efficiency in various applications.
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