Describe the exploration-exploitation trade-off in reinforcement learning.
The exploration-exploitation trade-off is a fundamental concept in reinforcement learning (RL) that refers to the dilemma faced by agents when deciding whether to explore new actions or exploit known actions to maximize their cumulative rewards. This trade-off arises because, in RL, agents must balance the desire to gather more information about the environment (exploration) with the goal of exploiting their current knowledge to achieve immediate rewards (exploitation).
