周雅情

个人信息

参与实验室科研项目

人机混合智能系统双层智能测试评估技术研究

复杂环境下非完全信息博弈决策的智能基础模型研究

研究课题

强化学习驱动机器下的人机控制方法研究

学术成果

共撰写/参与撰写专利 0 项，录用/发表论文 2 篇，投出待录用论文5篇。

Journal Articles

1. Human-in-the-Loop Reinforcement Learning with Risk-Aware Intervention and Imitation\hspace0pt Yaqing Zhou, and Yun-Bo Zhao Expert Systems With Applications 2026 [Abs] [pdf]

   Human-in-the-loop reinforcement learning (HiL-RL) improves policy safety and learning efficiency by incorporating real-time human interventions and demonstration data. However, existing HiL-RL methods often suffer from inaccurate intervention timing and inefficient use of demonstration data. To address these issues, we propose a novel framework called HiRIL (Human-in-the-loop Risk-aware Imitation-enhanced Learning), which establishes a closed-loop learning mechanism that integrates risk-aware intervention triggering and imitation-based policy optimization under a unified risk metric. At the core of HiRIL is the Bayesian Implicit Quantile Network (BIQN), which captures both epistemic and aleatoric uncertainty through Bayesian weight sampling and quantile-based return modeling. These uncertainties are combined to generate risk scores for state–action pairs, guiding when to trigger human intervention. To better utilize demonstration data, HiRIL introduces a prioritized experience replay mechanism based on risk difference, which emphasizes human interventions that significantly reduce risk. During policy optimization, a local imitation loss is applied to clone human actions at intervention points, enabling risk-guided joint optimization. We validate the effectiveness of HiRIL through extensive experiments on end-to-end autonomous driving tasks in the CARLA simulator. The results show that HiRIL achieves excellent performance in terms of training speed, task success rate, driving distance, and collision reduction, while maintaining strong robustness under control disturbances and non-expert interventions.

Book Chapters

1. Uncertainty-Based Dynamic Weighted Experience Replay for Human-in-the-Loop Deep Reinforcement Learning Xia Tian, Yu Kang, Yunbo Zhao, Yaqing Zhou, and Pengfei Li In Frontiers in Artificial Intelligence and Applications 2025 [Abs] [doi] [pdf]

   Human-in-the-loop reinforcement learning (HIRL) enhances sampling efficiency in deep reinforcement learning by incorporating human expertise and experience into the training process. However, HIRL methods still heavily depend on expert guidance, which is a key factor limiting their further development and largescale application. In this paper, an uncertainty-based dynamic weighted experience replay approach (UDWER) is proposed to solve the above problem. Our approach enables the algorithm to detect decision uncertainty, triggering human intervention only when uncertainty exceeds a threshold. This reduces the need for continuous human supervision. Additionally, we design a dynamic experience replay mechanism that prioritizes machine self-exploration and human-guided samples with different weights based on decision uncertainty. We also provide a theoretical derivation and related discussion. Experiments in the Lunar Lander environment demonstrate improved sampling efficiency and reduced reliance on human guidance.

博客文章