Near-Optimal Partially Observable Reinforcement Learning with Partial Online State Information

Ming Shi, Yingbin Liang, Ness B. Shroff

Partially observable Markov decision processes (POMDPs) are a general framework for sequential decision-making under latent state uncertainty, yet learning in POMDPs is intractable…