POMDP Planning

Partially Observable Markov Decision Processes (POMDPs) provide a framework for planning under uncertainty, aiming to find optimal actions given incomplete information about the environment's state. Current research focuses on improving the efficiency and safety of POMDP solvers, particularly for high-dimensional and continuous problems, employing techniques like Monte Carlo Tree Search (MCTS), particle filters (including Rao-Blackwellized variants), and neural network approximations for value functions and failure probabilities. These advancements address challenges in real-world applications such as robotics, autonomous driving, and safety-critical systems by enabling more efficient and reliable decision-making in complex, uncertain environments.