Latent MDPs

Latent Markov Decision Processes (LMDPs) model decision-making problems with hidden, persistent information, offering a framework for tackling partially observable environments. Current research focuses on developing efficient algorithms for learning and planning in LMDPs, particularly addressing challenges posed by long-term planning and high-dimensional latent spaces; Value Iteration Networks and diffusion probabilistic models are prominent approaches being explored. These advancements have implications for various fields, including reinforcement learning, medical image analysis, and natural language processing, by enabling more robust and efficient solutions to problems involving hidden states or unobserved information.