The field of communication networks is witnessing a significant shift towards AI-driven resource management, with a focus on optimizing performance, reliability, and scalability. Recent developments have highlighted the potential of innovative techniques such as optimistic learning, offline and distributional reinforcement learning, and hybrid reinforcement learning frameworks. These approaches aim to overcome the limitations of traditional online reinforcement learning methods and provide more efficient, adaptive, and robust solutions for resource management in communication networks. Notably, applications of these techniques have shown promising results in areas such as caching, edge computing, network slicing, and workload assignment. Furthermore, the use of AI and machine learning is being explored in the context of 6G networks, with a focus on ensuring scalability, reliability, privacy, ultra-low latency, and effective control. Some noteworthy papers in this area include:

• Optimistic Learning for Communication Networks, which introduces the concept of optimistic learning as a decision engine for resource management frameworks.
• Offline and Distributional Reinforcement Learning for Wireless Communications, which proposes a novel framework combining offline and distributional RL for wireless communication applications.
• A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling, which presents a hybrid RL framework for resource scheduling with hard latency constraints.
• Optimizing UAV Aerial Base Station Flights Using DRL-based Proximal Policy Optimization, which introduces an automated RL approach for optimizing UAV base station flights.
• Improving Offline Mixed-Criticality Scheduling with Reinforcement Learning, which develops an RL agent for scheduling mixed-criticality systems on processors with varying speeds.
• Cellular Network Design for UAV Corridors via Data-driven High-dimensional Bayesian Optimization, which addresses the challenge of designing cellular networks for UAV corridors through a novel data-driven approach.
• Context-aware Rate Adaptation for Predictive Flying Networks using Contextual Bandits, which proposes a novel Contextual Bandit-based approach for rate adaptation in predictive flying networks.