Temporal Difference Algorithm

Temporal difference (TD) learning is a core reinforcement learning algorithm aiming to estimate the value function of a policy by bootstrapping from sampled experience. Current research focuses on improving TD's convergence properties, particularly when using function approximation (e.g., neural networks or linear models), addressing challenges like slow convergence in long-horizon problems and instability with off-policy data. This involves developing novel algorithms, such as those incorporating PID control or adaptive step-size schedules, and analyzing the impact of model architectures like transformers on TD's performance and theoretical guarantees. Advances in TD learning directly impact the efficiency and robustness of reinforcement learning agents across various applications.