Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning
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Abstract (arXiv)
Ensemble and auxiliary tasks are both well known to improve the performance of machine learning models when data is limited. However, the interaction between these two methods is not well studied, particularly in the context of deep reinforcement learning. In this paper, we study the effects of ensemble and auxiliary tasks when combined with the deep Q-learning algorithm. We perform a case study on ATARI games under limited data constraint. Moreover, we derive a refined bias-variance-covariance decomposition to analyze the different ways of learning ensembles and using auxiliary tasks, and use the analysis to help provide some understanding of the case study. Our code is open source and available at https://github.com/NUS-LID/RENAULT.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 5 Jul 2021.
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20 of 30 references.
References · 20
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8 more
- 2018Model-Ensemble Trust-Region Policy Optimizationcited by 219
- 2020Never Give Up: Learning Directed Exploration Strategiescited by 212
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.