Trainify: A CEGAR-Driven Training and Verification Framework for Safe Deep Reinforcement Learning. - Peng Jin; Jiaxu Tian; Dapeng Zhi; Xuejun Wen; Min Zhang 002 (CAV (1), 2022)

BBReach: Tight and Scalable Black-Box Reachability Analysis of Deep Reinforcement Learning Systems. - Jiaxu Tian; Dapeng Zhi; Si Liu 003; Peixin Wang; Guy Katz; Min Zhang 002 (CoRR, 2022)

Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks. - Jiaxu Tian; Dapeng Zhi; Si Liu 003; Peixin Wang; Cheng Chen 015; Min Zhang 002 (NeurIPS, 2023)

Robustness Verification of Deep Reinforcement Learning Based Control Systems Using Reward Martingales. - Dapeng Zhi; Peixin Wang; Cheng Chen 015; Min Zhang 002 (AAAI, 2024)

Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems. - Dapeng Zhi; Peixin Wang; Si Liu 003; C.-H. Luke Ong; Min Zhang 002 (CAV (2), 2024)

Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training. - Jiaxu Tian; Dapeng Zhi; Si Liu 003; Peixin Wang; Guy Katz; Min Zhang 002 (VMCAI (2), 2024)

ATA: An Abstract-Train-Abstract approach for explanation-friendly deep reinforcement learning. - Shi Peng; Si Liu; Dapeng Zhi; Peixin Wang; Chenyang Xu 002; Cheng Chen; Min Zhang (Neural Networks, 2025)