The field of reinforcement learning and multi-agent systems is witnessing significant developments, with a focus on improving the interpretability and scalability of existing methods. Researchers are exploring novel approaches to optimize decision-making in complex environments, such as multi-level reinforcement learning and targeted intervention in multi-agent systems. Additionally, there is a growing interest in developing more efficient and effective algorithms for solving Markov decision processes and mixed-integer linear programming problems. Noteworthy papers in this area include: Learn to Change the World: Multi-level Reinforcement Learning with Model-Changing Actions, which introduces a new framework for reinforcement learning with model-changing actions. SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes, which proposes a novel method for computing decision tree policies in Markov decision processes. Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling, which enhances the SILVER framework to interpret deep reinforcement learning policies in high-dimensional and multi-action environments.