Value Function

Value functions, central to reinforcement learning and optimal control, estimate the expected cumulative reward from a given state or state-action pair, guiding agents towards optimal behavior. Current research focuses on improving value function approximation accuracy and stability, particularly using neural networks (including shallow ReLU networks and transformers), and developing algorithms that address challenges like offline learning, multi-task optimization, and robustness to noise and uncertainty. These advancements are crucial for enhancing the efficiency and reliability of reinforcement learning agents in diverse applications, from robotics and autonomous systems to personalized recommendations and safe AI.