In the context of reinforcement learning (RL), the concepts of exploration and exploitation represent two fundamental strategies that an agent employs to make decisions and learn optimal policies. These strategies are pivotal to the agent's ability to maximize cumulative rewards over time, and understanding the distinction between them is important for designing effective RL algorithms.