Safe Policy

Safe policy research focuses on developing algorithms that enable autonomous agents to learn optimal behaviors while rigorously adhering to safety constraints, crucial for deploying AI in real-world applications. Current research emphasizes efficient exploration strategies, often employing reinforcement learning with techniques like control barrier functions, subgoal decomposition, and adaptive regularization to balance reward maximization with safety guarantees. These advancements are improving sample efficiency and enabling formal verification of safety properties, leading to more reliable and trustworthy autonomous systems across robotics, autonomous driving, and other critical domains.