Panoramic videos have become more and more popular now. 360-degree videos give users a better experience but put forward a higher request for network at the same time. Many kinds of solutions to meet the high need of bandwidth have been proposed, such as tiled-based transmission, layer-based transmission and so on. Then how to choose the most suitable bitrates to make full use of the network resource is the problem to be solved urgently. In this paper, we propose a method based on reinforcement learning(RL) algorithm to select the bitrates of the region-of-interest adaptively for panoramic videos. We also compare RL algorithm with three traditional algorithms when changing the Field of View(FOV) and find that RL algorithm proves to perform better in minimizing the rebuffer time and providing higher quality video contents under various network conditions.