Generative Adversarial Imitation Learning from Failed Experiences (Student Abstract)


  • Jiacheng Zhu Soochow University
  • Jiahao Lin Soochow University
  • Meng Wang Fuxi AI Lab
  • Yingfeng Chen Fuxi AI Lab
  • Changjie Fan Fuxi AI Lab
  • Chong Jiang Soochow University
  • Zongzhang Zhang Nanjing University



Imitation learning provides a family of promising methods that learn policies from expert demonstrations directly. As a model-free and on-line imitation learning method, generative adversarial imitation learning (GAIL) generalizes well to unseen situations and can handle complex problems. In this paper, we propose a novel variant of GAIL called GAIL from failed experiences (GAILFE). GAILFE allows an agent to utilize failed experiences in the training process. Moreover, a constrained optimization objective is formalized in GAILFE to balance learning from given demonstrations and from self-generated failed experiences. Empirically, compared with GAIL, GAILFE can improve sample efficiency and learning speed over different tasks.




How to Cite

Zhu, J., Lin, J., Wang, M., Chen, Y., Fan, C., Jiang, C., & Zhang, Z. (2020). Generative Adversarial Imitation Learning from Failed Experiences (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13997-13998.



Student Abstract Track