【二】最新多智能体强化学习文章如何查阅｛顶会：AAAI、 ICML ｝

智能体强化学习文章如何查阅｛顶会：AAAI、 ICML ｝"/>

【二】最新多智能体强化学习文章如何查阅｛顶会：AAAI、 ICML ｝

相关文章：

【一】最新多智能体强化学习方法【总结】

【二】最新多智能体强化学习文章如何查阅｛顶会：AAAI、 ICML ｝

【三】多智能体强化学习（MARL）近年研究概览 ｛Analysis of emergent behaviors（行为分析)_、Learning communication（通信学习）｝

【四】多智能体强化学习（MARL）近年研究概览 ｛Learning cooperation（协作学习）、Agents modeling agents（智能体建模）｝

1.中国计算机学会(CCF)推荐国际学术会议和期刊目录

CCF官方网站

CCF推荐国际学术会议（参考链接：链接点击查阅具体分类）

类别如下计算机系统与高性能计算，计算机网络，网络与信息安全，软件工程，系统软件与程序设计语言，数据库、数据挖掘与内容检索，计算机科学理论，计算机图形学与多媒体，人工智能与模式识别，人机交互与普适计算，前沿、交叉与综合

2021 ICML 多智能体强化学习论文整理汇总

ICML地位：

1.1 中国计算机学会推荐国际学术会议
（人工智能与模式识别）

1.1.1 A类

1.1.2 B类

1.1.3 B、C类更多见附录

2.推荐深度强化学习实验室及链接

2.1 arXiv

arXiv是一个免费的分发服务和开放存取的档案，收录了物理、数学、计算机科学、定量生物学、定量金融、统计学、电气工程和系统科学以及经济学等领域的1,917,177篇学术文章。本网站上的材料没有经过arXiv的同行评审。

链接：/

 2.2 深度强化学习实验室

DeepRL——github：

微信公众号：Deep-RL

官网：/

论坛：/

2.3 AI 会议Deadlines

: 

2.4 ICML官网：

/

3.最新多智能体强化学习方向论文

3.1 ICML  International Conference on Machine Learning

[1]. Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

作者: Shariq Iqbal (University of Southern California) · Christian Schroeder (University of Oxford) · Bei Peng (University of Oxford) · Wendelin Boehmer (Delft University of Technology) · Shimon Whiteson (University of Oxford) · Fei Sha (Google Research)

[2]. UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

作者: Tarun Gupta (University of Oxford) · Anuj Mahajan (Dept. of Computer Science, University of Oxford) · Bei Peng (University of Oxford) · Wendelin Boehmer (Delft University of Technology) · Shimon Whiteson (University of Oxford)

[3]. Emergent Social Learning via Multi-agent Reinforcement Learning

作者: Kamal Ndousse (OpenAI) · Douglas Eck (Google Brain) · Sergey Levine (UC Berkeley) · Natasha Jaques (Google Brain, UC Berkeley)

[4]. DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

作者: Wei-Fang Sun (National Tsing Hua University) · Cheng-Kuang Lee (NVIDIA Corporation) · Chun-Yi Lee (National Tsing Hua University)

[5]. Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

作者: Iou-Jen Liu (University of Illinois at Urbana-Champaign) · Unnat Jain (UIUC) · Raymond Yeh (University of Illinois at Urbana–Champaign) · Alexander Schwing (UIUC)

[6]. Large-Scale Multi-Agent Deep FBSDEs

作者: Tianrong Chen (Georgia Institute of Technology) · Ziyi Wang (Georgia Institute of Technology) · Ioannis Exarchos (Stanford University) · Evangelos Theodorou (Georgia Tech)

[7]. Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning

作者: Anuj Mahajan (Dept. of Computer Science, University of Oxford) · Mikayel Samvelyan (University College London) · Lei Mao (NVIDIA) · Viktor Makoviychuk (NVIDIA) · Animesh Garg (University of Toronto, Vector Institute, Nvidia) · Jean Kossaifi (NVIDIA) · Shimon Whiteson (University of Oxford) · Yuke Zhu (University of Texas - Austin) · Anima Anandkumar (Caltech and NVIDIA)

[8]. Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

作者: Filippos Christianos (University of Edinburgh) · Georgios Papoudakis (The University of Edinburgh) · Muhammad Arrasy Rahman (The University of Edinburgh) · Stefano Albrecht (University of Edinburgh)

[9]. Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning

作者: Tung-Che Liang (Duke University) · Jin Zhou (Duke University) · Yun-Sheng Chan (National Chiao Tung University) · Tsung-Yi Ho (National Tsing Hua University) · Krishnendu Chakrabarty (Duke University) · Cy Lee (National Chiao Tung University)

[10]. A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

作者: Dong Ki Kim (MIT) · Miao Liu (IBM) · Matthew Riemer (IBM Research) · Chuangchuang Sun (MIT) · Marwa Abdulhai (MIT) · Golnaz Habibi (MIT) · Sebastian Lopez-Cot (MIT) · Gerald Tesauro (IBM Research) · Jonathan How (MIT)

[11]. Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

作者: Joel Z Leibo (DeepMind) · Edgar Duenez-Guzman (DeepMind) · Alexander Vezhnevets (DeepMind) · John Agapiou (DeepMind) · Peter Sunehag () · Raphael Koster (DeepMind) · Jayd Matyas (DeepMind) · Charles Beattie (DeepMind Technologies Limited) · Igor Mordatch (Google Brain) · Thore Graepel (DeepMind)

[12]. Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

作者: Luke Marris (DeepMind) · Paul Muller (DeepMind) · Marc Lanctot (DeepMind) · Karl Tuyls (DeepMind) · Thore Graepel (DeepMind)

[13]. Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition

作者: Bo Liu (University of Texas, Austin) · Qiang Liu (UT Austin) · Peter Stone (University of Texas at Austin) · Animesh Garg (University of Toronto, Vector Institute, Nvidia) · Yuke Zhu (University of Texas - Austin) · Anima Anandkumar (California Institute of Technology)

[14]. Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

作者: Matthieu Zimmer (Shanghai Jiao Tong University) · Claire Glanois (Shanghai Jiao Tong University) · Umer Siddique (Shanghai Jiao Tong University) · Paul Weng (Shanghai Jiao Tong University)

[15]. FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning

作者: Tianhao Zhang (Peking University) · yueheng li (Peking university) · Chen Wang (Peking University) · Zongqing Lu (Peking University) · Guangming Xie (1. State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University; 2. Institute of Ocean Research, Peking University)

3.2 AAAI Conference on Artificial Intelligence

会议时间节点

• August 15 – August 30, 2020: Authors register on the AAAI web site
• September 1, 2020: Electronic abstracts due at 11:59 PM UTC-12 (anywhere on earth)
• September 9, 2020: Electronic papers due at 11:59 PM UTC-12 (anywhere on earth)
• September 29, 2020: Abstracts AND full papers due for revisions of rejected NeurIPS/EMNLP submissions by 11:59 PM UTC-12 (anywhere on earth)
• AAAI-21 Reviewing Process: Two-Phase Reviewing and NeurIPS/EMNLP Fast Track Submissions
• November 3-5, 2020: Author Feedback Window (anywhere on earth)
• December 1, 2020: Notification of acceptance or rejection

具体论文见链接：

接收论文列表(共84篇)

4.附录

4.1 B类

4.2 C类