The field of optimization and reinforcement learning is moving towards more practical and real-world applications, with a focus on improving efficiency and safety in various industries such as manufacturing, mining, and logistics. Researchers are exploring innovative approaches, including the integration of reinforcement learning with other techniques like collision models and Monte Carlo planning, to address complex problems like container management and packing. Another trend is the development of configurable benchmarking environments, such as Mining-Gym, to facilitate the evaluation and comparison of reinforcement learning algorithms in realistic and dynamic settings. Additionally, there are advances in approximation algorithms for classic problems like the three-dimensional Knapsack problem, which have important implications for resource allocation and optimization in various industries. Notable papers include:

• Curriculum RL meets Monte Carlo Planning, which proposes a hybrid method for safe and efficient container management.
• Optimizing 2D+1 Packing in Constrained Environments Using Deep Reinforcement Learning, which demonstrates the potential of deep reinforcement learning for solving complex packaging problems.
• Improved Approximation Algorithms for Three-Dimensional Knapsack, which provides improved polynomial-time approximation algorithms for the 3DK problem and its variants.