## 2 AI Techniques in Communication Networks
### Machine Learning and Deep Learning in Network Applications
#### 2.1.3 Reinforcement Learning  
Reinforcement learning (RL) represents a unique approach in machine learning, where an agent learns optimal actions through interactions with its environment, receiving feedback in the form of rewards or penalties [49]. This makes RL particularly suitable for dynamic and evolving network environments where constant adaptation is required [50]. Unlike traditional machine learning models, which learn from static datasets, RL models continuously improve by interacting with the network environment, making them highly effective in scenarios that involve real-time decision-making, resource allocation, and optimization. In dynamic wireless networks, one of the most prominent applications of RL is in spectrum management. Deep Q-Networks (DQNs), a popular variant of RL, have shown great promise in allocating radio spectrum resources efficiently [51]. Using continuous learning from feedback signals, the RL agent adjusts its actions to maximize network throughput, minimize latency, or optimize energy usage, depending on the specific objective. This enables the network to autonomously adapt to varying traffic loads, interference levels, or other environmental factors without requiring human intervention [52]. The RL agent is not simply reacting to current conditions, but also learning over time, improving its actions based on the evolving network state. Another key application of RL in network environments is in adaptive routing and congestion control. RL algorithms can learn to select the most efficient data transmission paths in real-time, accounting for dynamic changes in network conditions. The agent updates its routing policies based on feedback, allowing for more efficient use of resources and reducing bottlenecks that can result in delays or packet loss [53]. This real-time adaptability is essential for maintaining high levels of performance in networks that face constant changes in topology or traffic patterns. Additionally, RL can optimize load balancing across different parts of the network, ensuring that resources are distributed efficiently to avoid overloads and ensure smooth communication [54]. One of the main advantages of RL over traditional machine learning techniques is its ability to handle sequential decision-making problems, where the outcome of each action is influenced by previous actions. This is crucial in network environments where long-term planning and decision-making are necessary. For example, in autonomous network management, RL allows the system to continuously learn and adapt its policies based on previous experiences, improving its decision-making capabilities over time. This is particularly valuable in scenarios such as managing energy consumption in large-scale networks, where RL can optimize power usage over extended periods, considering both immediate and future network conditions [55]. The versatility of RL in dynamic network environments makes it an essential tool for future-proofing networks, particularly as the complexity of networks continues to grow. RL's ability to optimize a wide range of network parameters--such as throughput, latency, energy consumption, and reliability--ensures that it can adapt to the evolving needs of modern communication systems. Assessment of RL's Potential in Network Research:  
The direction of current research in RL for network environments is focused on enhancing the scalability and efficiency of RL models to handle large, complex networks. As the size and diversity of networks grow, RL techniques must evolve to address challenges such as slow convergence rates, high computational costs, and the difficulty of training models in real-time. Recent advancements are exploring the use of Multi-Agent RL (MARL), where multiple agents collaborate or compete to optimize network performance in scenarios like multi-user resource allocation or cooperative routing [56].