Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

Jul 12, 2020

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According to previous studies, one of the major impediments to accurate off-policy learning is the overestimation bias. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting. Our method—Truncated Quantile Critics, TQC,—blends three ideas: distributional representation of a critic, truncation of critics prediction, and ensembling of multiple critics. We show that all components are key for the achieved performance. Distributional representation combined with truncation allows for arbitrary granular overestimation control, and ensembling further improves the results of our method. TQC significantly outperforms the current state of the art on all environments from the continuous control benchmark suite, demonstrating 25

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The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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