Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation

Sergio Rozada, Jose Luis Orejuela, Antonio G. Marques

We study the problem of learning optimal policies in finite-horizon Markov Decision Processes (MDPs) using low-rank reinforcement learning (RL) methods. In finite-horizon MDPs, the…