This paper provides a comprehensive survey of deep reinforcement learning (DRL) algorithms and their applications in autonomous driving (AD). It begins by outlining the components of an AD system, including perception, localization, mapping, planning, and control. The paper then delves into the fundamentals of reinforcement learning (RL), discussing key concepts such as Markov decision processes (MDPs), value-based methods (e.g., Q-learning), policy-based methods (e.g., policy gradient), and actor-critic methods. It also explores extensions to RL, including reward shaping, multi-agent RL, multi-objective RL, state representation learning, and learning from demonstrations. The paper highlights the challenges and opportunities in deploying RL for real-world AD systems, emphasizing the importance of addressing computational challenges and ensuring safety. Finally, it reviews specific AD tasks where DRL has been applied, such as controller optimization, path planning, motion planning, and high-level driving policy development.This paper provides a comprehensive survey of deep reinforcement learning (DRL) algorithms and their applications in autonomous driving (AD). It begins by outlining the components of an AD system, including perception, localization, mapping, planning, and control. The paper then delves into the fundamentals of reinforcement learning (RL), discussing key concepts such as Markov decision processes (MDPs), value-based methods (e.g., Q-learning), policy-based methods (e.g., policy gradient), and actor-critic methods. It also explores extensions to RL, including reward shaping, multi-agent RL, multi-objective RL, state representation learning, and learning from demonstrations. The paper highlights the challenges and opportunities in deploying RL for real-world AD systems, emphasizing the importance of addressing computational challenges and ensuring safety. Finally, it reviews specific AD tasks where DRL has been applied, such as controller optimization, path planning, motion planning, and high-level driving policy development.