The recent developments in the research area of reinforcement learning and optimization in network and supply chain management highlight a significant shift towards integrating deep learning techniques with traditional optimization methods to address complex, dynamic problems. Innovations are particularly focused on enhancing decision-making processes under uncertainty, improving computational efficiency, and ensuring robust performance across varying scenarios. The integration of Deep Reinforcement Learning (DRL) with domain-specific knowledge and novel neural network architectures is proving to be a promising direction, offering solutions that outperform traditional methods in adaptability and efficiency. Additionally, the application of these advanced techniques in real-world scenarios, such as pharmaceutical supply chains and vehicular networks, underscores the practical relevance and potential impact of these research efforts.

Noteworthy papers include:

• A study on inventory control policies for pharmaceutical supply chains, which benchmarks classical and DRL policies, demonstrating the potential of integrating diverse policies to manage complex challenges effectively.
• The introduction of a switch-type neural network architecture for resource allocation problems, which significantly improves the efficiency and generalization of DRL policies in queueing networks.
• A novel dynamic improvement framework for vehicular task offloading, which optimizes task offloading in environments with random velocities, showcasing significant performance gains over traditional methods.
• A transformer-based DQN approach for dynamic load balancing in SDNs, which significantly enhances network performance by integrating accurate traffic prediction with intelligent routing decisions.
• A joint task offloading and user scheduling framework for 5G MEC systems under jamming attacks, which effectively minimizes delay and dropped tasks, demonstrating superior performance in mitigating security threats.