Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. (arXiv:2206.12020v1 [cs.LG])

We study Reinforcement Learning for partially observable dynamical systems
using function approximation. We propose a new \textit{Partially Observable
Bilinear Actor-Critic framework}, that is general enough to include models such
as observable tabular Partially Observable Markov Decision Processes (POMDPs),
observable Linear-Quadratic-Gaussian (LQG), Predictive State Representations
(PSRs), as well as a newly introduced model Hilbert Space Embeddings of POMDPs
and observable POMDPs with latent low-rank transition. Under this framework, we
propose an actor-critic style algorithm that is capable of performing agnostic
policy …

arxiv learning lg observable reinforcement reinforcement learning systems