Continuous POMDP

Continuous Partially Observable Markov Decision Processes (POMDPs) address sequential decision-making under uncertainty where states, actions, and observations are continuous, posing significant computational challenges. Current research focuses on developing efficient approximation methods, including those based on particle filtering, adaptive discretization of action spaces (e.g., using Voronoi trees), and linear programming, to solve these complex problems, often incorporating belief-dependent rewards and constraints. These advancements are crucial for tackling real-world applications like robotics, railway maintenance, and other safety-critical systems where accurate and timely decision-making in uncertain environments is paramount.