The increasing complexity of user preferences in group activities, such as movie watching, necessitates more dynamic recommendation systems. SQUIRREL, utilizing Reinforcement Learning, adapts to the changing preferences of group members through sequential group recommendations. This paper introduces an enhancement to SQUIRREL by permitting the re-recommendation of items, reflecting more realistic scenarios where users may revisit previously enjoyed content. We implemented and evaluated three distinct reward functions—focusing on maximizing group satisfaction, balancing satisfaction with diversity, and maximizing diversity—to investigate their impact on recommendation diversity and group satisfaction. Experimental results, using the 20M MovieLens dataset, demonstrate that while diversity-focused rewards enhance recommendation variety, it requires careful balance with user satisfaction to avoid diminishing user engagement.