Belief MDP

Belief Markov Decision Processes (Belief MDPs) address decision-making in partially observable environments by representing uncertainty using belief states—probability distributions over possible system states. Current research focuses on improving the efficiency and scalability of Belief MDP solutions, particularly through the development of model-free agent-state based approaches (e.g., using recurrent neural networks) and approximate methods that offer performance guarantees despite simplifying complex observation models or transition dynamics. This work is significant for enabling robust decision-making in real-world applications like robotics and autonomous systems where perfect state information is unavailable, leading to more reliable and efficient planning algorithms.