The field of autonomous decision-making and optimization is rapidly advancing, with a focus on developing innovative methods for complex problem-solving. Recent research has explored the use of neural networks, reinforcement learning, and graph neural networks to improve decision-making in various domains, including semiconductor manufacturing, healthcare, and team formation. These approaches have shown promising results in terms of efficiency, scalability, and adaptability. Notably, the integration of symbolic formalisms, such as Linear Temporal Logic, with neural networks has enabled more explainable and effective decision-making. Furthermore, the development of novel frameworks, such as Neural Algorithmic Reasoning and Graph Neural Algorithmic Reasoning, has expanded the capabilities of autonomous decision-making systems. Some noteworthy papers in this area include: KNARsack, which demonstrates the effectiveness of neural algorithmic reasoners in solving pseudo-polynomial problems, and Tackling GNARLy Problems, which introduces a reinforcement learning-based approach to graph neural algorithmic reasoning. Fully Learnable Neural Reward Machines is also a significant contribution, as it enables the learning of both the Symbol Grounding function and the automaton end-to-end, removing the need for prior knowledge.