Heuristic Policy

Heuristic policies, rules of thumb designed to guide decision-making in complex systems, are a focus of current research across diverse fields. Researchers are exploring how to improve these policies, often by integrating them with machine learning techniques like reinforcement learning and imitation learning, particularly using neural network architectures such as graph neural networks and attention-based models. This work aims to enhance the efficiency and scalability of heuristic policies in applications ranging from multi-agent pathfinding and resource allocation to e-commerce logistics and emergency response, ultimately leading to more effective and adaptable automated systems.