POMDP Solver

Partially Observable Markov Decision Processes (POMDPs) provide a mathematical framework for planning under uncertainty, aiming to find optimal decision strategies when an agent's knowledge of the environment is incomplete. Current research focuses on developing efficient solution algorithms, including point-based methods, deep reinforcement learning approaches, and Monte Carlo tree search, often incorporating techniques to handle high-dimensional state and observation spaces or to address model uncertainty. These advancements are improving the applicability of POMDPs to complex real-world problems, such as robotics, resource management, and healthcare, by enabling more robust and efficient decision-making in uncertain environments. The development of guaranteed approximate solutions and efficient handling of large-scale problems are key areas of ongoing investigation.