Value Iteration

Value iteration is an iterative dynamic programming algorithm used to find optimal policies in Markov Decision Processes (MDPs), a fundamental framework for sequential decision-making under uncertainty. Current research focuses on improving value iteration's efficiency and robustness, particularly for long-horizon problems and those with large state spaces, through techniques like matrix deflation, PID control, and entropy regularization, as well as adapting it for use with neural networks (e.g., Value Iteration Networks) and in multi-agent settings. These advancements enhance the applicability of value iteration to complex real-world problems in areas such as robotics, resource management, and reinforcement learning, where efficient and reliable planning is crucial.