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Towards robust and domain agnostic reinforcement learning competitions

Jun 07, 2021
William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge Ritter, Chengjie Wu, Xiaotian Hao, Yiming Lu, Hangyu Mao, Yihuan Mao, Chao Wang, Michal Opanowicz, Anssi Kanervisto, Yanick Schraner, Christian Scheller, Xiren Zhou, Lu Liu, Daichi Nishio, Toi Tsuneda, Karolis Ramanauskas, Gabija Juceviciute

* 20 pages, several figures, published PMLR 

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Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

Jun 03, 2021
Tianze Zhou, Fubiao Zhang, Kun Shao, Kai Li, Wenhan Huang, Jun Luo, Weixun Wang, Yaodong Yang, Hangyu Mao, Bin Wang, Dong Li, Wulong Liu, Jianye Hao

* 12 pages, 9 figures 

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Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing

Mar 05, 2020
Hangyu Mao, Zhibo Gong, Zhen Xiao

* cover

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Learning Agent Communication under Limited Bandwidth by Message Pruning

Dec 03, 2019
Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, Yan Ni

* accepted as a regular paper with poster presentation @ AAAI20. arXiv admin note: text overlap with arXiv:1903.05561 

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Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

Dec 03, 2019
Hangyu Mao, Wulong Liu, Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang, Jun Wang, Zhen Xiao

* accepted as a regular paper with oral presentation @ AAAI20 

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Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing

Feb 26, 2019
Hangyu Mao, Zhibo Gong, Zhengchao Zhang, Zhen Xiao, Yan Ni

* This paper proposes a gating mechanism with several crucial designs for adaptively prunning the unprofitable communication messages among multiple agents, such that the limited-bandwidth restriction existing in many real-world muli-agent systems can be resolved. Experiments show that our method can prune quite a lot of unprofitable messages with little damage to the performance 

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Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG

Nov 13, 2018
Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong

* Attention-based Multi-agent DDPG. Experimental results show that it not only outperforms the state-of-the-art RL-based methods and rule-based methods by a large margin, but also achieves better performance in terms of scalability and robustness 

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ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning

Oct 29, 2017
Hangyu Mao, Zhibo Gong, Yan Ni, Zhen Xiao

* V3 of original submission. Actor-Critic Method for Multi-agent Learning-to-Communicate based on Deep Reinforcement Learning, It is suitable for both continuous and discrete action space environments 

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