Aug 28, 2018 · In this paper, we address the largely open problem of how to obtain these high-confidence estimates, for general state-spaces.
Estimating the value function for a fixed pol- icy is a fundamental problem in reinforcement learning. Policy evaluation algorithms—to es-.
High-confidence error estimates for learned value functions
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A high-confidence bound on an empirical estimate of the value error to the true value error is provided, which is used to design an offline sampling ...
The hypercorrection effect refers to the finding that when given corrective feedback, errors that are committed with high confidence are easier to correct than ...
Feb 17, 2024 · A confidence interval is a method that computes an upper and a lower bound around an estimated value. The actual parameter value is either insider or outside ...
Three experiments investigated whether the hypercorrection effect – the finding that errors committed with high confidence are easier, rather than more ...
... High-confidence error estimates for learned value functions | Estimating the value function for a fixed policy is a fundamental problem in reinforcement ...
Our results provide evidence that confidence-based learning signals affect instrumentally learned subjective values in the absence of external feedback.
Abstract—Most value function learning algorithms in reinforcement learning are based on the mean squared (projected) Bellman error.
Apr 4, 2020 · In this paper, we combine these two conditions to construct tests for admissible functions in reward design using available data. This yields a ...