A new two-trajectory sampling algorithm for average-reward TD learning guarantees convergence with quadratic sample complexity and no explicit dimension dependence in both tabular and linear approximation settings.
On the performance of temporal difference learning with neural networks
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Bridging the Gap Between Average and Discounted TD Learning
A new two-trajectory sampling algorithm for average-reward TD learning guarantees convergence with quadratic sample complexity and no explicit dimension dependence in both tabular and linear approximation settings.