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arXiv · 2110.13523

Automating Control of Overestimation Bias for Reinforcement Learning

Arsenii Kuznetsov·Alexander Grishin·Artem Tsypin·Arsenii Ashukha·Artur Kadurin·Dmitry Vetrov
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Abstract

Overestimation bias control techniques are used by the majority of high-performing off-policy reinforcement learning algorithms. However, most of these techniques rely on pre-defined bias correction policies that are either not flexible enough or require environment-specific tuning of hyperparameters. In this work, we present a general data-driven approach for the automatic selection of bias control hyperparameters. We demonstrate its effectiveness on three algorithms: Truncated Quantile Critics, Weighted Delayed DDPG, and Maxmin Q-learning. The proposed technique eliminates the need for an extensive hyperparameter search. We show that it leads to a significant reduction of the actual number of interactions while preserving the performance.

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Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin, Arsenii Ashukha, Artur Kadurin, Dmitry Vetrov. 2021-10-26. Automating Control of Overestimation Bias for Reinforcement Learning. https://arxiv.org/abs/2110.13523

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