Bellman Equation

The Bellman equation is a fundamental concept in reinforcement learning (RL), providing a recursive relationship for calculating optimal value functions that guide an agent's decision-making process. Current research focuses on improving the efficiency and accuracy of solving the Bellman equation, particularly in high-dimensional and continuous spaces, using techniques like function approximation with neural networks, Koopman operators, and novel regularization methods to control model complexity. These advancements are crucial for scaling RL to complex real-world problems, impacting fields such as robotics, control systems, and resource management by enabling more efficient and robust learning algorithms.