Smoking remains one of the largest health concerns worldwide, which is why eHealth applications with virtual coaches have been developed to assist smokers with quitting. Providing additional feedback from human coaches during such smoking cessation programs can further improve the effectiveness of the intervention. However, due to budgetary constraints and the limited availability of human coaches, it is important to make informed decisions about when someone gets human support to optimize the effectiveness. This research investigates the use of reinforcement learning (RL) to determine when to provide human feedback in quitting smoking with a virtual coach. Using data from a longitudinal study, we implemented an RL model that decides when to involve a human coach based on users' appreciation for human support and their self-efficacy, optimizing the effort that people spend on preparatory activities and their likelihood of returning to the program. Results show that the model is effective in allocating human support, increasing users' effort and return likelihood while considering the cost of human coaches. These findings support using RL to help with determining when to provide human support in smoking cessation programs.