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A maximum-entropy approach to off-policy evaluation in average-reward MDPs
[article]
2020
arXiv
pre-print
This work focuses on off-policy evaluation (OPE) with function approximation in infinite-horizon undiscounted Markov decision processes (MDPs). For MDPs that are ergodic and linear (i.e. where rewards and dynamics are linear in some known features), we provide the first finite-sample OPE error bound, extending existing results beyond the episodic and discounted cases. In a more general setting, when the feature dynamics are approximately linear and for arbitrary rewards, we propose a new
arXiv:2006.12620v1
fatcat:v3uohickvjhvnmrhc6wdlmm7gu