Linear Function Approximation

Linear function approximation aims to efficiently represent value functions in reinforcement learning and other sequential decision-making problems by using linear combinations of features. Current research emphasizes developing provably efficient algorithms for various settings, including those with off-policy learning, delayed feedback, heavy-tailed rewards, and multiple agents, often employing techniques like temporal difference learning, fitted Q-iteration, and natural policy gradients. These advancements address the computational challenges of large state spaces and improve the theoretical understanding and practical performance of reinforcement learning algorithms, impacting fields like robotics, control systems, and AI safety.