Sample-Optimal Parametric Q-Learning Using Linearly Additive Features

13 Feb 2019 Lin F. Yang Mengdi Wang

Consider a Markov decision process (MDP) that admits a set of state-action features, which can linearly express the process's probabilistic transition model. We propose a parametric Q-learning algorithm that finds an approximate-optimal policy using a sample size proportional to the feature dimension $K$ and invariant with respect to the size of the state space... (read more)

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