Shared Autonomous Vehicle (SAV) has many impacts on the transport development, such as saving parking space. However, SAV meets a huge challenge in terms of vehicle supply and user demand imbalance. The traditional mathematical optimization method cannot be well used due to the computational burden. Hence, this paper proposes a Reinforcement Learning (RL) based SAV relocation approach. First, two types of RL agents, car-based and zone-based agents, are developed as agents for vehicles and stations, respectively. Then, the RL scheme is trained by using historical demand data to facilitate real-time carsharing relocation. Finally, to compare the proposed two types of RL methods, three scenarios are used: small-scale, middle-scale, and large-scale networks. Solutions indicate that the enhanced zone-based method achieves an additional 146% profit compared to traditional threshold-based relocation strategies. The user travel behaviour impacts are provided by analyzing parking demand and travel movements among residential, industrial and commercial zones.