What is the exploration vs. exploitation dilemma in reinforcement learning?

The exploration vs. exploitation dilemma in reinforcement learning refers specifically to the balance between trying out new actions (exploration) and choosing the best-known action (exploitation). This dilemma is a fundamental challenge in the field, as an agent must decide whether to use what it has already learned to maximize immediate rewards or to explore new possibilities to potentially discover better long-term strategies.

The essence of this dilemma lies in the need for the agent to gather information about its environment while also leveraging its current knowledge to maximize rewards. If an agent only exploits known actions, it may miss out on better options that could yield higher rewards in the future. Conversely, if it spends too much time exploring, it may not earn enough rewards, which could hinder its performance.

In contrast, training with new versus existing data does not capture this dilemma. The other options also mischaracterize the core concept: understanding user preferences versus enhancing exploration isn't directly related to reinforcement learning actions, while overfitting versus underfitting pertains to model performance rather than decision-making behavior in exploration and exploitation.