Q Function

The Q-function, central to reinforcement learning, estimates the expected cumulative reward for taking a specific action in a given state. Current research focuses on improving Q-function estimation accuracy and efficiency, particularly through variance reduction techniques, and exploring its application in diverse settings such as multi-agent systems, continuous action spaces, and large language model alignment. These advancements are driving progress in offline reinforcement learning, enabling more efficient and robust decision-making in complex environments and leading to improved performance in various applications, including robotics and healthcare.