Smart Sampling: Self-Attention and Bootstrapping for Improved Ensembled Q-Learning

• URL: http://arxiv.org/abs/2405.08252v1
• Date: Tue, 14 May 2024 00:57:02 GMT
• Title: Smart Sampling: Self-Attention and Bootstrapping for Improved Ensembled Q-Learning
• Authors: Muhammad Junaid Khan, Syed Hammad Ahmed, Gita Sukthankar,
• Abstract summary: We present a novel method aimed at enhancing the sample efficiency of ensemble Q learning. Our proposed approach integrates multi-head self-attention into the ensembled Q networks while bootstrapping the state-action pairs ingested by the ensemble.
• Score: 0.6963971634605796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel method aimed at enhancing the sample efficiency of ensemble Q learning. Our proposed approach integrates multi-head self-attention into the ensembled Q networks while bootstrapping the state-action pairs ingested by the ensemble. This not only results in performance improvements over the original REDQ (Chen et al. 2021) and its variant DroQ (Hi-raoka et al. 2022), thereby enhancing Q predictions, but also effectively reduces both the average normalized bias and standard deviation of normalized bias within Q-function ensembles. Importantly, our method also performs well even in scenarios with a low update-to-data (UTD) ratio. Notably, the implementation of our proposed method is straightforward, requiring minimal modifications to the base model.

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