Cloud-Edge Collaboration for Continual Reinforcement Learning aims to leverage cloud and edge resources to create more efficient and adaptive reinforcement learning systems. One of the key challenges in deploying cloud-trained models to edge devices is ensuring that the models can effectively adapt to real-world, time-varying conditions while minimizing issues like overestimation bias. Recall that in Chap. 3 , in order to reduce the suboptimality gap in Warm-Start Reinforcement Learning, it is essential to reduce the bias in the approximation error, which can be achieved by using techniques such as ensemble learning and planning. In this chapter, we introduce Adaptive Ensemble Q-learning (AdaEQ) as a promising solution to enhance cloud-edge collaboration for Continual Reinforcement Learning. AdaEQ addresses the overestimation issue in Q-learning by using an ensemble of Q-function approximators to estimate action values.