A Guided-to-Autonomous Policy Learning Method of Deep Reinforcement Learning in Path Planning. - Wang Zhao 003; Ye Zhang 020; Haoyu Li (ICCA, 2024)

A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability. - Ye Zhang 020; Yutong Zhu; Haoyu Li; Jingyu Wang 002 (Neurocomputing, 2024)

Recent progress, challenges and future prospects of applied deep reinforcement learning : A practical perspective in path planning. - Ye Zhang 020; Wang Zhao 003; Jingyu Wang 002; Yuan Yuan (Neurocomputing, 2024)

EPPE: An Efficient Progressive Policy Enhancement framework of deep reinforcement learning in path planning. - Wang Zhao 003; Ye Zhang 020; Zikang Xie (Neurocomputing, 2024)

Global optimization of interception guidance law for maneuvering target based on reward reconstruction. - Wang Zhao 003; Qinglong Zhang; Ye Zhang 020; Jingyu Wang 002 (Expert Syst. Appl., 2025)

Gaussian-enhanced reinforcement learning for scalable evasion strategies in multi-agent pursuit-evasion games. - Ye Zhang 020; Yutong Zhu; Jingyu Wang 002 (Neurocomputing, 2025)

A probabilistic safety-aware strategy in stochastic multi-agent reach-avoid games. - Ye Zhang 020; Yutong Zhu; Huanhuan Yuan; Yuan Yuan (J. Frankl. Inst., 2025)

Stochastic reachability-integrated flexible formation control for multi-agent systems. - Yutong Zhu; Ye Zhang 020 (J. Frankl. Inst., 2026)