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MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far.
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Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni · 2016
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Mastering the game of Go with deep neural networks and tree search
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Victor do Nascimento Silva and Luiz Chaimowicz · 2017
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Mastering the game of Go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Learning attentional communication for multi-agent cooperation
Jiechuan Jiang and Zongqing Lu · 2018
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Igor Mordatch and Pieter Abbeel · 2018
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Hierarchical macro strategy model for moba game ai
Bin Wu · 2019
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Learning efficient multi-agent communication: An information bottleneck approach
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Learning when to communicate at scale in multi-agent cooperative and competitive tasks
Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar · 2018
Cited alongside, same era.
On the utility of learning about humans for human-ai coordination
Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, and Anca Dragan · 2019
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Tarmac: Targeted multi-agent communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau · 2019
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Human-level performance in 3D multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C Rabinowitz, Ari S Morcos, Avraham Ruderman, et al · 2019
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Learning to schedule communication in multi-agent reinforcement learning
Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, and Yung Yi · 2019
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Human-robot collaboration in disassembly for sustainable manufacturing
Quan Liu, Zhihao Liu, Wenjun Xu, Quan Tang, Zude Zhou, and Duc Truong Pham · 2019
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Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, and Zinovi Rabinovich · 2020
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Trajectory diversity for zero-shot coordination
Lupu Andrei, Cui Brandon, Hu Hengyuan, and Foerster Jakob N · 2021
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Tencent shows off artificial intelligence with honor of kings ai competition, 2021
Hongyu Chen · 2021
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Learning diverse policies in moba games via macro-goals
Yiming Gao, Bei Shi, Xueying Du, Liang Wang, Guangwei Chen, Zhenjie Lian, Fuhao Qiu, Guoan Han, Weixuan Wang, Deheng Ye, et al · 2021
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Dynamic population-based meta-learning for multi-agent communication with natural language
Abhinav Gupta, Marc Lanctot, and Angeliki Lazaridou · 2021
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Pay attention to mlps
Hanxiao Liu, Zihang Dai, David So, and Quoc V Le · 2021
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Evaluation of human-ai teams for learned and rule-based agents in hanabi
Ho Chit Siu, Jaime Peña, Edenna Chen, Yutai Zhou, Victor Lopez, Kyle Palko, Kimberlee Chang, and Ross Allen · 2021
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Collaborating with humans without human data
DJ Strouse, Kevin McKee, Matt Botvinick, Edward Hughes, and Richard Everett · 2021
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Symbols as a lingua franca for bridging human-ai chasm for explainable and advisable ai systems
Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, and Lin Guan · 2022
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Warmth and competence in human-agent cooperation
Kevin R McKee, Xuechunzi Bai, and Susan T Fiske · 2022
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