Even the successful advantages of the Q-learning algorithm have been used. However, one of the key disadvantages of Q-learning is costly for the agent in the early stages of learning since each state-action combination must be entered consistently to converge on the best policy. Deep learning has been proposed as a potential game-changer. It has the potential to solve RL's limits, entering a new paradigm of RL known as Deep Reinforcement Learning (DRL). Deep Neural Networks (DNNs) are used to train the learning phase in DRL, which accelerates the learning process and improves the performance of RL algorithms. As a result, DRL has been used in computer vision, robotics, speech recognition, and natural language processing, among other practice-based RL applications. One of the most well-known uses of DRL is AlphaGo, which is the first computer program to defeat a human expert without handicaps on a full-sized 19x19 board.

DRL is a new instrument in communications and networking that may be used to handle various problems and obstacles successfully. Modern networks are becoming more Ad-hoc, decentralized, and autonomous, such as the Heterogeneous Networks (HetNets), Internet of Things (IoT), and Unmanned Aerial Vehicles (UAV). Network entities such as mobile users, IoT devices, and UAVs must make decisions on a local and autonomous level, data rate selection, such as spectrum access and transmit power control.

DRL techniques provide the following benefits:
1. DRL makes it possible for network entities to understand the communication and networking environment better. Thus, without knowing the mobility pattern or the channel model, mobile users may learn optimum policies such as caching, base station selection, channel selection, offloading decisions, and handover decisions using DRL.
2. DRL is capable of providing complete network optimization solutions. As a result, it allows network controllers in modern networks, such as base stations, to solve non-convex and complex issues like joint user association, computation, and transmission scheduling without extensive and exact network information.
3. DRL significantly enhances learning speed, especially in the circumstances with large state and action spaces. As a result, DRL allows network controllers or IoT gateways to manage user association, spectrum access dynamically, and transmit power for many IoT devices and mobile users in large-scale networks, such as IoT systems with thousands of devices.