This chapter explores an intersection of deep learning and RL as the driving force behind the existence of an autonomous AI. Reinforcement learning is considered to be able to provide the framework for learning agents from interaction with their environment, as well as enhance the model's ability to handle complicated high-dimensional data. Deep RL has thus emerged to be one of the most critical technologies that allow machines to make decisions on their own and adapt themselves to dynamic environments. This chapter introduces the basic ideas of RL agents, environments, and rewards, and relates those to the advances brought by deep learning, such as DQN and policy gradient methods. Real-world applications include but are not limited to self-driving cars, robotics, finance, and healthcare. This shows the promise of learning and acting systems. Further, it delves into such issues as training stability, design of rewards, and ethics. Finally, it looks forward to even more related research in AI autonomy, pointing the reader toward a future whereby deep RL will be incorporated into an eventual next-generation intelligent machine, self-correcting, and self-modifying.