Characterizes utility functions making recursive OCE objectives PAC-learnable and derives matching upper and lower PAC sample complexity bounds for value and policy learning, with improved tau dependence for CVaR.
University of London, University College London (United Kingdom), 2003
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On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents
Characterizes utility functions making recursive OCE objectives PAC-learnable and derives matching upper and lower PAC sample complexity bounds for value and policy learning, with improved tau dependence for CVaR.