Exploration vs Exploitation is a dilemma in reinforcement learning where the agent must try new actions (explore) versus using known profitable actions (exploit). Balancing these behaviors is crucial for finding optimal policies. Techniques like epsilon-greedy and Upper Confidence Bounds address this balance.