Publications 📖

Accepted

1. Fandi Gou, Haikuo Du, Chenyu Zhao and Yunze Cai, A Policy-Guided Reinforcement Learning Method for Encirclement Control in Multiobstacle Environment, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 36, no. 9, pp. 17034-17046, 2025.
   • See brief work description here.
2. Fandi Gou, Chenyu Zhao, Haikuo Du and Yunze Cai, Modeling Deception in Multi-Robot Target-Attacker-Defender Game via Deep Reinforcement Learning, in Proc IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Jun 2025, accepted.
   • See brief work description here.

Under Review

1. Fandi Gou, Chenyu Zhao, Hengyuan Zhao and Yunze Cai, Guide Policy Assisted Reinforcement Learning for Multi-agent Tasks under Restricted Communication. Submitted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Apr 2025, major revision.
   • See a brief work description here.
2. Fandi Gou, Haikuo Du and Yunze Cai, A Graph-Based Safe Reinforcement Learning Method for Multi-agent Cooperation. Submitted to Neural Networks (NN), Jun 2025, major revision.
   • See a brief work description here.
3. Fandi Gou, Hengyuan Zhao, Chen Yan and Yunze Cai, Graph-based Formation Control with Collision Avoidance via Policy-Guided Reinforcement Learning. Submitted to IEEE Robotics and Automation Letters (RA-L), Sep 2025, under review.
   • See a brief work description here.

In Preparation

1. Fandi Gou, Chenyu Zhao, Hengyuan Zhao and Yunze Cai, - Primal-Dual based Safe Reinforcement Learning for Multi-Agent Navigation with Graph Information Aggregation. Preparing to submit to International Federation of Automatic Control World Congress 2026 (IFAC), Nov 2025.
   • See a brief work description here.