Low-rank tensor approximations of value functions enable tractable policy iteration for finite-horizon MDPs, with BCD/BCGD algorithms, convergence guarantees, and error bounds linking evaluation to policy improvement.
Low-rank tensor methods for Markov chains with applications to tumor progression models,
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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation
Low-rank tensor approximations of value functions enable tractable policy iteration for finite-horizon MDPs, with BCD/BCGD algorithms, convergence guarantees, and error bounds linking evaluation to policy improvement.