The recent research in the field of sustainable and resilient infrastructure has seen significant advancements, particularly in the areas of disaster-resilient communication networks, reinforcement learning applications, and energy-efficient transportation solutions. The focus has been on developing technologies that not only withstand and recover from disasters but also contribute to sustainability goals through energy efficiency and economic feasibility. Innovations in reinforcement learning have been pivotal, addressing complex problems such as non-Markovian transitions and non-stationary dynamics within decision-making processes. Additionally, the optimization of electric vehicle routing and charging has seen substantial improvements, with models that consider real-world vehicle characteristics and dynamic speed optimization algorithms. These developments underscore the importance of integrating advanced technologies with practical applications to enhance the resilience and efficiency of critical systems. Notably, the integration of machine learning into smart grids and the exploration of hydrogen's role in decarbonizing energy systems have also been significant areas of focus, highlighting the potential for transformative impacts on energy management and sustainability.

Noteworthy Papers:

• Harnessing Causality in Reinforcement Learning With Bagged Decision Times: Introduces a novel approach to handle non-Markovian transitions within decision-making processes using causal DAGs.
• Green vehicle routing problem that jointly optimizes delivery speed and routing based on the characteristics of electric vehicles: Proposes an energy consumption model and a speed optimization algorithm for more efficient electric vehicle routing.
• Role of hydrogen in decarbonizing China's electricity and hard-to-abate sectors: Examines the cost implications of green hydrogen in fully decarbonizing energy systems, highlighting its potential cost savings.