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• W3208952172 abstract "Bias correction techniques are used by most of the high-performing methods for off-policy reinforcement learning. However, these techniques rely on a pre-defined bias correction policy that is either not flexible enough or requires environment-specific tuning of hyperparameters. In this work, we present a simple data-driven approach for guiding bias correction. We demonstrate its effectiveness on the Truncated Quantile Critics -- a state-of-the-art continuous control algorithm. The proposed technique can adjust the bias correction across environments automatically. As a result, it eliminates the need for an extensive hyperparameter search, significantly reducing the actual number of interactions and computation." @default.
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• W3208952172 date "2021-10-26" @default.
• W3208952172 modified "2023-09-27" @default.
• W3208952172 title "Automating Control of Overestimation Bias for Continuous Reinforcement Learning" @default.
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