Based Policy

Policy-based approaches are increasingly used to optimize decision-making across diverse fields, aiming to learn optimal strategies from data or simulations. Current research focuses on improving policy learning efficiency and robustness through techniques like reinforcement learning (RL), contextual bandits, and multi-agent RL, often employing neural networks, graph neural networks, and diffusion models. These advancements are impacting various sectors, from autonomous systems and supply chain optimization to healthcare and traffic management, by enabling more efficient, adaptable, and safer decision-making systems. Furthermore, research emphasizes addressing challenges like safety verification, handling uncertainty, and explaining learned policies for improved transparency and trust.