Bukharin, Alexander, Li, Yan, Yu, Yue, Zhang, Qingru, Chen, Zhehui, Zuo, Simiao, Zhang, Chao, Zhang, Songan, and Zhao, Tuo. Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms. Retrieved from https://par.nsf.gov/biblio/10471452. Conference on Neural Information Processing Systems .

Bukharin, Alexander, Li, Yan, Yu, Yue, Zhang, Qingru, Chen, Zhehui, Zuo, Simiao, Zhang, Chao, Zhang, Songan, & Zhao, Tuo. Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms. Conference on Neural Information Processing Systems, (). Retrieved from https://par.nsf.gov/biblio/10471452.

Bukharin, Alexander, Li, Yan, Yu, Yue, Zhang, Qingru, Chen, Zhehui, Zuo, Simiao, Zhang, Chao, Zhang, Songan, and Zhao, Tuo. "Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms". Conference on Neural Information Processing Systems (). Country unknown/Code not available: Advances in Neural Information Processing Systems. https://par.nsf.gov/biblio/10471452.

@article{osti_10471452,
place = {Country unknown/Code not available}, title = {Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms}, url = {https://par.nsf.gov/biblio/10471452}, abstractNote = {}, journal = {Conference on Neural Information Processing Systems}, publisher = {Advances in Neural Information Processing Systems}, author = {Bukharin, Alexander and Li, Yan and Yu, Yue and Zhang, Qingru and Chen, Zhehui and Zuo, Simiao and Zhang, Chao and Zhang, Songan and Zhao, Tuo.}, }