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Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning. arXiv preprint arXiv:2008.10103 ( 2020 ). Shuang Qiu, Zhuoran Yang, Xiaohan Wei, Jieping Ye, and Zhaoran Wang. 2020. Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning. arXiv preprint arXiv:2008.10103 (2020)."},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of Iternational Symposium on Information Processing in Sensor Networks. 20--27","author":"Rabbat Michael","year":"2004","unstructured":"Michael Rabbat and Robert Nowak . 2004 . Distributed optimization in sensor networks . In Proceedings of Iternational Symposium on Information Processing in Sensor Networks. 20--27 . Michael Rabbat and Robert Nowak. 2004. Distributed optimization in sensor networks. In Proceedings of Iternational Symposium on Information Processing in Sensor Networks. 20--27."},{"key":"e_1_3_2_2_39_1","volume-title":"Information consensus in multivehicle cooperative control","author":"Ren Wei","year":"2007","unstructured":"Wei Ren , Randal W Beard , and Ella M Atkins . 2007. Information consensus in multivehicle cooperative control . IEEE Control systems magazine 27, 2 ( 2007 ), 71--82. Wei Ren, Randal W Beard, and Ella M Atkins. 2007. Information consensus in multivehicle cooperative control. IEEE Control systems magazine 27, 2 (2007), 71--82."},{"key":"e_1_3_2_2_40_1","volume-title":"The International Conference on Information Network","author":"Rhee Seung Hyong","year":"2012","unstructured":"Seung Hyong Rhee , Hwa-Sung Kim , and Seung-Won Sohn . 2012 . The effect of decentralized resource allocation in network-centric warfare . In The International Conference on Information Network 2012. IEEE, 478--481. Seung Hyong Rhee, Hwa-Sung Kim, and Seung-Won Sohn. 2012. The effect of decentralized resource allocation in network-centric warfare. In The International Conference on Information Network 2012. IEEE, 478--481."},{"key":"e_1_3_2_2_41_1","volume-title":"Fairness gan. arXiv preprint arXiv:1805.09910","author":"Sattigeri Prasanna","year":"2018","unstructured":"Prasanna Sattigeri , Samuel C Hoffman , Vijil Chenthamarakshan , and Kush R Varshney . 2018. Fairness gan. arXiv preprint arXiv:1805.09910 ( 2018 ). Prasanna Sattigeri, Samuel C Hoffman, Vijil Chenthamarakshan, and Kush R Varshney. 2018. Fairness gan. arXiv preprint arXiv:1805.09910 (2018)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-018-01357-w"},{"key":"e_1_3_2_2_43_1","volume-title":"Building trust in artificial intelligence, machine learning, and robotics. Cutter business technology journal 31, 2","author":"Siau Keng","year":"2018","unstructured":"Keng Siau and Weiyu Wang . 2018. Building trust in artificial intelligence, machine learning, and robotics. Cutter business technology journal 31, 2 ( 2018 ), 47--53. Keng Siau and Weiyu Wang. 2018. Building trust in artificial intelligence, machine learning, and robotics. Cutter business technology journal 31, 2 (2018), 47--53."},{"key":"e_1_3_2_2_44_1","volume-title":"Proceedings of IEEE International Conference on Robotics and Automation","volume":"4","author":"Smart William D","year":"2002","unstructured":"William D Smart and L Pack Kaelbling . 2002 . Effective reinforcement learning for mobile robots . In Proceedings of IEEE International Conference on Robotics and Automation , Vol. 4 . 3404--3410. William D Smart and L Pack Kaelbling. 2002. Effective reinforcement learning for mobile robots. In Proceedings of IEEE International Conference on Robotics and Automation, Vol. 4. 3404--3410."},{"key":"e_1_3_2_2_45_1","volume-title":"Proceedings of International Conference on Machine Learning. 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Multi-agent reinforcement learning via double averaging primal-dual optimization. arXiv preprint arXiv:1806.00877 (2018)."},{"key":"e_1_3_2_2_48_1","volume-title":"Statistical decision functions which minimize the maximum risk. Annals of Mathematics","author":"Wald Abraham","year":"1945","unstructured":"Abraham Wald . 1945. Statistical decision functions which minimize the maximum risk. Annals of Mathematics ( 1945 ), 265--280. Abraham Wald. 1945. Statistical decision functions which minimize the maximum risk. Annals of Mathematics (1945), 265--280."},{"key":"e_1_3_2_2_49_1","volume-title":"Distributed stochastic multi-task learning with graph regularization. arXiv preprint arXiv:1802.03830","author":"Wang Weiran","year":"2018","unstructured":"Weiran Wang , Jialei Wang , Mladen Kolar , and Nathan Srebro . 2018. Distributed stochastic multi-task learning with graph regularization. arXiv preprint arXiv:1802.03830 ( 2018 ). Weiran Wang, Jialei Wang, Mladen Kolar, and Nathan Srebro. 2018. 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Enhanced first and zeroth order variance reduced algorithms for min-max optimization. arXiv preprint arXiv:2006.09361 (2020)."},{"key":"e_1_3_2_2_53_1","volume-title":"Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach. arXiv preprint arXiv:1905.11549","author":"Zhang Xin","year":"2019","unstructured":"Xin Zhang , Jia Liu , and Zhengyuan Zhu . 2019. Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach. arXiv preprint arXiv:1905.11549 ( 2019 ). Xin Zhang, Jia Liu, and Zhengyuan Zhu. 2019. Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach. arXiv preprint arXiv:1905.11549 (2019)."},{"key":"e_1_3_2_2_54_1","first-page":"18825","article-title":"Taming communication and sample complexities in decentralized policy evaluation for cooperative multi-agent reinforcement learning","volume":"34","author":"Zhang Xin","year":"2021","unstructured":"Xin Zhang , Zhuqing Liu , Jia Liu , Zhengyuan Zhu , and Songtao Lu . 2021 . Taming communication and sample complexities in decentralized policy evaluation for cooperative multi-agent reinforcement learning . Advances in Neural Information Processing Systems 34 (2021), 18825 -- 18838 . Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, and Songtao Lu. 2021. Taming communication and sample complexities in decentralized policy evaluation for cooperative multi-agent reinforcement learning. 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