This paper presents a comprehensive survey on the applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, such as IoT and UAV networks, are becoming more decentralized and autonomous, requiring network entities to make local decisions to maximize network performance under uncertain conditions. Traditional reinforcement learning (RL) is limited in large-scale networks due to the complexity of state and action spaces. DRL, combining RL with deep learning, addresses these challenges by using deep neural networks to improve learning speed and performance. The paper reviews DRL applications in dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation. It also discusses applications in traffic routing, resource sharing, and data collection. Key challenges and future research directions are highlighted. DRL is effective in solving complex problems in communications and networking, such as games modeled as non-cooperative games, where it can find Nash equilibria without complete information. The paper also introduces advanced DRL models, including double DQL, prioritized experience replay, dueling DQL, asynchronous multi-step DQL, distributional DQL, and noisy nets. These models improve the efficiency and performance of DRL in various applications. The survey emphasizes the importance of DRL in next-generation networks like 5G and beyond, and highlights the need for further research in this area.This paper presents a comprehensive survey on the applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, such as IoT and UAV networks, are becoming more decentralized and autonomous, requiring network entities to make local decisions to maximize network performance under uncertain conditions. Traditional reinforcement learning (RL) is limited in large-scale networks due to the complexity of state and action spaces. DRL, combining RL with deep learning, addresses these challenges by using deep neural networks to improve learning speed and performance. The paper reviews DRL applications in dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation. It also discusses applications in traffic routing, resource sharing, and data collection. Key challenges and future research directions are highlighted. DRL is effective in solving complex problems in communications and networking, such as games modeled as non-cooperative games, where it can find Nash equilibria without complete information. The paper also introduces advanced DRL models, including double DQL, prioritized experience replay, dueling DQL, asynchronous multi-step DQL, distributional DQL, and noisy nets. These models improve the efficiency and performance of DRL in various applications. The survey emphasizes the importance of DRL in next-generation networks like 5G and beyond, and highlights the need for further research in this area.