Music streaming services have transformed the way people listen to music in recent years. The current streaming services majorly rely on collaborative and hybrid filtering techniques, which predominantly recommend popular songs. However, most present systems lack musical contents, user taste changes, and novelty parameters. In this paper, we propose a music recommendation using reinforcement learning with personalizing the individual results. The proposed method implements a Q-learning model derived from the incremental reinforcement learning algorithm based on the cumulative reward from similar songs played and liked during the session. The user profile is modeled using implicit and explicit feedback from the individual musical interactions with the system. The cumulative reward obtained from the experimental outcomes demonstrates that a combination of reinforcement learning and personalized recommendation potentially broadens the scope of recommendations by including freshness and novelty. The experimental result shows average interaction time improvement of 35% compared with existing apps.