AI RESEARCH
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
arXiv CS.LG
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ArXi:2603.07437v1 Announce Type: new We study the problem of state representation learning for control from partial and potentially high-dimensional observations. We approach this problem via cost-driven state representation learning, in which we learn a dynamical model in a latent state space by predicting cumulative costs. In particular, we establish finite-sample guarantees on finding a near-optimal representation function and a near-optimal controller using the learned latent model for infinite-horizon time-invariant Linear Quadratic Gaussian (LQG) control.