Reinforcement learning (RL) has significantly advanced the way computers perform complex tasks, particularly in autonomous driving systems, which is like teaching a computer to figure out the best way to do something in complex situations. This paper provides an overview of various reinforcement learning techniques and their applications in self-driving car tasks. It also examines the key challenges associated with implementing these methods in real-world scenarios. It also looks at how computer simulations are used to train these systems and ways to make sure they work correctly and reliably.