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.
Truncated variance reduced value iteration
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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.