Monte Carlo Planning

Monte Carlo planning is a family of algorithms that address complex decision-making problems by combining tree-based search with Monte Carlo sampling to estimate the value of different actions. Current research focuses on improving efficiency and robustness, particularly within the context of partially observable Markov decision processes (POMDPs) and constrained Markov decision processes (MDPs), often employing algorithms like Monte Carlo Tree Search (MCTS) and incorporating neural networks for function approximation. These advancements are impacting diverse fields, including robotics, economics, and AI, by enabling more efficient and reliable planning in uncertain and resource-constrained environments. The development of scalable and theoretically grounded methods remains a key focus.