Temporal Difference Learning

Temporal difference (TD) learning is a core reinforcement learning algorithm aiming to efficiently estimate the value function of a policy by bootstrapping from temporally successive predictions. Current research emphasizes improving TD's convergence properties, particularly for scenarios involving function approximation (e.g., using neural networks) and off-policy data, with a focus on developing provably convergent algorithms and analyzing their finite-sample behavior. These advancements are significant for both theoretical understanding of reinforcement learning and practical applications, enabling more stable and efficient training of agents in complex environments across diverse fields like robotics, autonomous driving, and control systems.