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7 December 2023 Weighted bootstrapped DQN: efficient exploration via uncertainty quantification
Jinhui Pang, Zicong Feng
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Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129411J (2023) https://doi.org/10.1117/12.3012024
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China
Abstract
Uncertainty quantification is an essential method for sample-efficient deep reinforcement learning. There is a growing literature on uncertainty-based deep reinforcement learning algorithms, but many of the previous approaches failed to capture different sources of uncertainty. We highlight why this can be a crucial shortcoming for sample-efficient algorithms and provide a sophisticated analysis of the uncertainty in the interaction between agent and environment. Based on that, we propose Weighted Bootstrapped DQN, an exploration-efficient method that combines network ensembles and variance weighting. We use aleatoric uncertainty estimation together with epistemic uncertainty to improve the exploration ability of the algorithm. We prove that our new approach has a significant improvement in sample efficiency on different gym tasks, even compared with the previous state-of-the-art approaches.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jinhui Pang and Zicong Feng "Weighted bootstrapped DQN: efficient exploration via uncertainty quantification", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129411J (7 December 2023); https://doi.org/10.1117/12.3012024
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KEYWORDS
Neural networks
Deep learning
Error analysis
Statistical modeling
Uncertainty analysis
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