Prior Policy

Prior policy research focuses on leveraging past experiences or pre-defined rules to improve decision-making in various contexts, from reinforcement learning to policy optimization and even legal document analysis. Current research emphasizes efficient methods for incorporating prior knowledge, including the development of novel algorithms like those based on policy gradients, Bayesian optimization, and inductive logic programming, often implemented using neural networks or graph-based models. This work has significant implications for improving the efficiency and safety of AI systems, enabling more robust and adaptable agents, and facilitating better understanding and automation of complex decision-making processes across diverse fields.