HTTP-based streaming has become the dominant technology for streaming due to the widespread adoption of the HTTP protocol. Many streaming providers use a combination of Content Delivery Network (CDN) and Viewer-to-Viewer (V2V) technology, known as Hybrid CDN/V2V live streaming, for both efficiency and cost-effectiveness. V2V technology allows for offloading streaming traffic from the CDN and reducing operational costs, and WebRTC technology facilitates direct V2V transfer, as it is natively supported by all browsers. In a WebRTC-based V2V network, some viewers cache the video chunks on their devices, while others wait and fetch chunks from their neighbors. A common strategy used to determine when a viewer should stop waiting for chunk delivery and revert to the CDN is called Random Waiting Time Control (RWC). However, due to the complex dynamics in the V2V system, RWC is far from optimal. In this work, we have formulated the Waiting Time Control determination problem as a reinforcement learning problem and proposed a Q-learning-based Waiting Time Control (QWC) solution. We conducted offline experiments in the Grid5000 [1] testbed and validated our results through a 14-day A/B testing in the wild. Our findings showed that QWC improves overall streaming Qualityof-Experience (QoE) in rebuffering (-29% fewer events), video quality (+17% higher), and buffer length (+5% longer), with a slightly improved V2V ratio (+5% more).