C POMDPs

Partially Observable Markov Decision Processes (POMDPs) model decision-making under uncertainty where the agent only has partial knowledge of the environment's state. Current research focuses on improving the efficiency and scalability of POMDP solution methods, particularly for high-dimensional problems, often employing deep generative models, hierarchical reinforcement learning, and policy gradient methods to address the computational challenges. These advancements are driving progress in various applications, including autonomous driving, robotics, and healthcare, where handling uncertainty and incomplete information is crucial for effective decision-making. Furthermore, research explores robust solutions that account for imprecise probabilities and address issues like delayed observations and prolonged action effects.