最新多智能体强化学习文章如何查阅{顶会:AAAI ICML }
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【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
类别名称 | 数量 |
---|---|
投稿量 | 5513 |
接收量 | 1184 |
强化学习方向文章 | 163 |
其中多智能体强化学习文章 | 15 |
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
序号 | 会议简称 | 会议全称 | 出版社 | 网址 |
1 | AAAI | AAAI Conference on Artificial Intelligence | AAAI | |
2 | CVPR | IEEE Conference on Computer Vision and | IEEE | |
3 | ICCV | International Conference on Computer | IEEE | |
4 | ICML | International Conference on Machine | ACM | |
5 | IJCAI | International Joint Conference on Artificial | Morgan Kaufmann |
1.1.2 B类
序号 | 会议简称 | 会议全称 | 出版社 | 网址 |
1 | COLT | Annual Conference on Computational | Springer | |
2 | NIPS | Annual Conference on Neural Information | MIT Press |
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。
2.2 深度强化学习实验室
DeepRL——github:https://github.com/neurondance
微信公众号:Deep-RL
官网:http://www.neurondance.com/
论坛:http://deeprl.neurondance.com/
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:http://deeprl.neurondance.com/d/191-82aaai2021
接收论文列表(共84篇)
4.附录
4.1 B类
序号 | 会议简称 | 会议全称 | 出版社 | 网址 |
1 | COLT | Annual Conference on Computational | Springer | |
2 | NIPS | Annual Conference on Neural Information | MIT Press | |
3 | ACL | Annual Meeting of the Association for | ACL | |
4 | EMNLP | Conference on Empirical Methods in Natural | ACL | |
5 | ECAI | European Conference on Artificial | IOS Press | |
6 | ECCV | European Conference on Computer Vision | Springer | |
7 | ICRA | IEEE International Conference on Robotics | IEEE | |
8 | ICAPS | International Conference on Automated | AAAI | |
9 | ICCBR | International Conference on Case-Based | Springer | |
10 | COLING | International Conference on Computational | ACM | |
11 | KR | International Conference on Principles of | Morgan Kaufmann | |
12 | UAI | International Conference on Uncertainty | AUAI | |
13 | AAMAS | International Joint Conference | Springer |
4.2 C类
序号 | 会议简称 | 会议全称 | 出版社 | 网址 |
1 | ACCV | Asian Conference on Computer Vision | Springer | |
2 | CoNLL | Conference on Natural Language Learning | CoNLL | |
3 | GECCO | Genetic and Evolutionary Computation | ACM | |
4 | ICTAI | IEEE International Conference on Tools with | IEEE | |
5 | ALT | International Conference on Algorithmic | Springer | |
6 | ICANN | International Conference on Artificial Neural | Springer | |
7 | FGR | International Conference on Automatic Face | IEEE | |
8 | ICDAR | International Conference on Document | IEEE | |
9 | ILP | International Conference on Inductive Logic | Springer | |
10 | KSEM | International conference on Knowledge | Springer | |
11 | ICONIP | International Conference on Neural | Springer | |
12 | ICPR | International Conference on Pattern | IEEE | |
13 | ICB | International Joint Conference on Biometrics | IEEE | |
14 | IJCNN | International Joint Conference on Neural | IEEE | |
15 | PRICAI | Pacific Rim International Conference on | Springer | |
16 | NAACL | The Annual Conference of the North | NAACL | |
17 | BMVC | British Machine Vision Conference | British Machine |
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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)}