Online POMDP Solver

Online POMDP solvers aim to efficiently find optimal actions in partially observable environments, a crucial challenge in robotics and AI. Current research focuses on improving scalability and efficiency through techniques like adaptive discretization of continuous action spaces (e.g., using Voronoi trees), robust learning methods handling uncertainty in transition dynamics, and the incorporation of learned heuristics to guide the search process (e.g., using inductive logic programming). These advancements enable more effective planning in complex, high-dimensional scenarios, with applications ranging from robotic manipulation to resource management, by providing provably efficient and robust solutions to challenging real-world problems.