Human-in-the-loop reinforcement learning in continuous-action space
Abstract
Human-in-the-loop for reinforcement learning (RL) is usually employed to overcome the challenge of sample inefficiency, in which the human expert provides advice for the agent when necessary. The current human-in-the-loop RL (HRL) results mainly focus on discrete action space. In this article, we propose a Q value-dependent policy (QDP)-based HRL (QDP-HRL) algorithm for continuous action space. Considering the cognitive costs of human monitoring, the human expert only selectively gives advice in the early stage of agent learning, where the agent implements human-advised action instead. The QDP framework is adapted to the twin delayed deep deterministic policy gradient algorithm (TD3) in this article for the convenience of comparison with the state-of-the-art TD3. Specifically, the human expert in the QDP-HRL considers giving advice in the case that the difference between the twin Q -networks' output exceeds the maximum difference in the current queue. Moreover, to guide the update of the critic network, the advantage loss function is developed using expert experience and agent policy, which provides the learning direction for the QDP-HRL algorithm to some extent. To verify the effectiveness of QDP-HRL, the experiments are conducted on several continuous action space tasks in the OpenAI gym environment, and the results demonstrate that QDP-HRL greatly improves learning speed and performance.
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Ethical considerations and legal compliance have become increasingly crucial as AI technologies evolve and integrate into sectors, requiring focus on developing 'Ethical AI Frameworks'.
“As AI technologies rapidly evolve and become more integrated into various sectors, ethical considerations and legal compliance have become increasingly crucial, which requires a focus on developing 'Ethical AI Frameworks', a new category in our taxonomy reflecting the trend towards responsible AI development in generative AI [214], [215], [15], [216], [217].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [215]
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- Authors
- Luo, B., Wu, Z., Zhou, F., & Wang, B.-C.
- Affiliations
- Central South University (CN)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2024-11
- Volume
- 35
- Issue
- 11
- Pages
- 15735-15744
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W4383503699
- Cited by
- 41
- References
- 55
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 8 · 2023: 4
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62022094, 61873350)
- Zhejiang Laboratory (2021NB0AB01)
- Hunan Provincial Natural Science Foundation of China (2020JJ2049)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- human-in-the-loop reinforcement learning, continuous action spaces, TD3, sample inefficiency
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