On Convergence of Emphatic Temporal-Difference Learning

8 Jun 2015Huizhen Yu

We consider emphatic temporal-difference learning algorithms for policy evaluation in discounted Markov decision processes with finite spaces. Such algorithms were recently proposed by Sutton, Mahmood, and White (2015) as an improved solution to the problem of divergence of off-policy temporal-difference learning with linear function approximation... (read more)

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