High-confidence error estimates for learned value functions
Touqir Sajed,
Wesley Chung,
Martha White
Abstract:Estimating the value function for a fixed policy is a fundamental problem in reinforcement learning. Policy evaluation algorithms-to estimate value functions-continue to be developed, to improve convergence rates, improve stability and handle variability, particularly for off-policy learning. To understand the properties of these algorithms, the experimenter needs high-confidence estimates of the accuracy of the learned value functions. For environments with small, finite state-spaces, like chains, the true va… Show more
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