This paper is the first to explore the integration of Kolmogorov-Arnold networks (KANs) into off-policy methods for continuous reinforcement learning (CRL) tasks. We introduce KAN-SAC, a method that integrates the KAN model and its variants, namely MultKAN and SineKAN, with the Soft Actor-Critic (SAC) algorithm. The integration is based on the embedding of the KAN architecture in both actor and critic networks. Using the Mujoco Half-Cheetah environment as a case study, we evaluate the performance of these KAN-based SAC algorithms against traditional MLP-based SAC. Our results show that KAN models have great potential, even outperforming MLP models in certain scenarios. However, further refinement of these methods is needed before they can be used as a robust alternative in complex CRL applications.