Reinforcement learning (RL) has emerged as a pivotal technology for autonomous driving, providing a framework where an agent learns optimal decision-making through environment interaction without pre-defined labels. This review focuses on the application of RL in autonomous driving systems, exploring key concepts such as value-based methods, policy-based methods, and actor-critic methods within the context of Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). We discuss the integration of these methods in navigating the complex, dynamic environments encountered in autonomous driving, addressing challenges in perception, sensing, decision-making, and control. The review further evaluates the role of simulators in testing and refining RL algorithms, highlighting their importance in the development cycle of autonomous vehicles.