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

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

http://www.aaai.org

2

CVPR

IEEE Conference on Computer Vision and 
Pattern Recognition

IEEE

http://www.pamitc.org/cvpr13/

3

ICCV

International Conference on Computer
Vision

IEEE

http://www.iccv2013.org/

4

ICML

International Conference on Machine 
Learning

ACM

http://icml.cc/2013/

5

IJCAI

International Joint Conference on Artificial
Intelligence

Morgan Kaufmann

http://www.ijcai.org

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
Learning Theory

Springer

http://orfe.princeton.edu/conferences/colt2013/

2

NIPS

Annual Conference on Neural Information
Processing Systems

MIT Press

http://www.nips.cc

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:https://arxiv.org/

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

https://aideadlin.es

2.4 ICML官网:

https://icml.cc/

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
Learning Theory

Springer

http://orfe.princeton.edu/conferences/colt2013/

2

NIPS

Annual Conference on Neural Information
Processing Systems

MIT Press

http://www.nips.cc

3

ACL

Annual Meeting of the Association for 
Computational Linguistics

ACL

http://acl2013.org/site/index.html

4

EMNLP

Conference on Empirical Methods in Natural
Language Processing

ACL

http://www.sigdat.org/

5

ECAI

European Conference on Artificial 
Intelligence

IOS Press

http://www.ecai2013.upit.ro/?i=2542

6

ECCV

European Conference on Computer Vision

Springer

http://eccv2012.unifi.it/

7

ICRA

IEEE International Conference on Robotics
and Automation

IEEE

http://www.icra2013.org/

8

ICAPS

International Conference on Automated
Planning and Scheduling

AAAI

http://www.icaps-conference.org/

9

ICCBR

International Conference on Case-Based
Reasoning

Springer

http://www.iccbr.org/

10

COLING

International Conference on Computational
Linguistics

ACM

http://www.coling2012-iitb.org/

11

KR

International Conference on Principles of
Knowledge Representation and Reasoning

Morgan Kaufmann

http://www.kr.org/

12

UAI

International Conference on Uncertainty
in Artificial Intelligence

AUAI

http://auai.org/

13

AAMAS

International Joint Conference
on Autonomous Agents and Multi-agent
Systems

Springer

http://www.aamas-conference.org/

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

http://www.accv2012.org/

2

CoNLL

Conference on Natural Language Learning

CoNLL

http://www.clips.ua.ac.be/conll/

3

GECCO

Genetic and Evolutionary Computation
Conference

ACM

http://www.sigevo.org/gecco-2013/

4

ICTAI

IEEE International Conference on Tools with
Artificial Intelligence

IEEE

http://ictai12.unipi.gr/

5

ALT

International Conference on Algorithmic
Learning Theory

Springer

http://www-alg.ist.hokudai.ac.jp/~thomas/ALT13/

6

ICANN

International Conference on Artificial Neural
Networks

Springer

https://www.waset.org/conferences/2013/
amsterdam/icann/

7

FGR

International Conference on Automatic Face
and Gesture Recognition

IEEE

http://fg2013.cse.sc.edu/

8

ICDAR

International Conference on Document
Analysis and Recognition

IEEE

http://www.icdar2013.org/

9

ILP

International Conference on Inductive Logic
Programming

Springer

http://ilp13.cos.ufrj.br/

10

KSEM

International conference on Knowledge
Science,Engineering and Management

Springer

http://ksem.dlut.edu.cn/

11

ICONIP

International Conference on Neural 
Information Processing

Springer

http://iconip2013.org/

12

ICPR

International Conference on Pattern 
Recognition

IEEE

http://www.icpr2014.org/

13

ICB

International Joint Conference on Biometrics

IEEE

http://atvs.ii.uam.es/icb2013/

14

IJCNN

International Joint Conference on Neural
Networks

IEEE

http://www.ijcnn2013.org/

15

PRICAI

Pacific Rim International Conference on 
Artificial Intelligence

Springer

http://ktw.mimos.my/pricai2012/

16

NAACL

The Annual Conference of the North
American Chapter of the Association 
for Computational Linguistics

NAACL

http://naacl2013.naacl.org/

17

BMVC

British Machine Vision Conference

British Machine
Vision 
Association

http://bmvc2013.bristol.ac.uk/

【二】最新多智能体强化学习文章如何查阅{顶会:AAAI、 ICML }的更多相关文章

  1. ICML 2018 | 从强化学习到生成模型:40篇值得一读的论文

    https://blog.csdn.net/y80gDg1/article/details/81463731 感谢阅读腾讯AI Lab微信号第34篇文章.当地时间 7 月 10-15 日,第 35 届 ...

  2. (转) 深度强化学习综述:从AlphaGo背后的力量到学习资源分享(附论文)

    本文转自:http://mp.weixin.qq.com/s/aAHbybdbs_GtY8OyU6h5WA 专题 | 深度强化学习综述:从AlphaGo背后的力量到学习资源分享(附论文) 原创 201 ...

  3. [强化学习]Part1:强化学习初印象

    引入 智能 人工智能 强化学习初印象 强化学习的相关资料 经典书籍推荐:<Reinforcement Learning:An Introduction(强化学习导论)>(强化学习教父Ric ...

  4. Deep Learning专栏--强化学习之从 Policy Gradient 到 A3C(3)

    在之前的强化学习文章里,我们讲到了经典的MDP模型来描述强化学习,其解法包括value iteration和policy iteration,这类经典解法基于已知的转移概率矩阵P,而在实际应用中,我们 ...

  5. 强化学习——如何提升样本效率 ( DeepMind 综述深度强化学习:智能体和人类相似度竟然如此高!)

    强化学习     如何提升样本效率 参考文章: https://news.html5.qq.com/article?ch=901201&tabId=0&tagId=0&docI ...

  6. 强化学习之二:Q-Learning原理及表与神经网络的实现(Q-Learning with Tables and Neural Networks)

    本文是对Arthur Juliani在Medium平台发布的强化学习系列教程的个人中文翻译.(This article is my personal translation for the tutor ...

  7. 强化学习调参技巧二:DDPG、TD3、SAC算法为例:

    1.训练环境如何正确编写 强化学习里的 env.reset() env.step() 就是训练环境.其编写流程如下: 1.1 初始阶段: 先写一个简化版的训练环境.把任务难度降到最低,确保一定能正常训 ...

  8. 强化学习(二)马尔科夫决策过程(MDP)

    在强化学习(一)模型基础中,我们讲到了强化学习模型的8个基本要素.但是仅凭这些要素还是无法使用强化学习来帮助我们解决问题的, 在讲到模型训练前,模型的简化也很重要,这一篇主要就是讲如何利用马尔科夫决策 ...

  9. 【转载】 强化学习(二)马尔科夫决策过程(MDP)

    原文地址: https://www.cnblogs.com/pinard/p/9426283.html ------------------------------------------------ ...

  10. 强化学习二:Markov Processes

    一.前言 在第一章强化学习简介中,我们提到强化学习过程可以看做一系列的state.reward.action的组合.本章我们将要介绍马尔科夫决策过程(Markov Decision Processes ...

随机推荐

  1. pytest+request+allure生成测试报告

    基本流程 模拟数据 url,paras,method,except http://www.baidu.com, {k=12}, get, 200 请求url (接口文档) 参数 请求方法 预期返回响应 ...

  2. .NET Moq mock internal类型

    问题 Can not create proxy for type xxx because type xxx is not accessible. Make it public, or internal ...

  3. 玩转AIGC,5分钟 Serverless 部署 Stable Diffustion 服务

    有没有一种可能,其实你早就在AIGC了?阿里云将提供免费Serverless函数计算产品资源,邀请你,体验一把AIGC级的毕加索.达芬奇.梵高等大师作画的快感.下面请尽情发挥你的想象空间!!双重奖品设 ...

  4. 第三届云原生编程挑战赛正式启动,Serverless 赛道邀你参加!

    据<云原生开发现状报告>显示,全球云原生开发人员达 680 万,与 2020 年 5 月报告的云原生开发者数量 470 万相比,全球云原生开发人员数量正极速增长,越来越多开发者加入到云原生 ...

  5. SpringBoot-mybatisplus-模糊查询

    模糊查询如何实现如下案例中两种实现方法 第一种:利用QueryWrapper.like自己实现. 第二种:使用@TableField(condition = SqlCondition.LIKE)实现. ...

  6. vue-router路由复用后页面没有刷新

    vue-router提供了页面路由功能,可以用来构建单页面应用,在使用vue-router的动态路由匹配的时候,遇到了url变化了,但是页面却没有任何动静的问题,记录一下. 动态路由匹配url变化了, ...

  7. P1802-DP【橙】

    1.又是一道因为写了异常剪枝而调了好久的题,以后再也不写异常剪枝了,异常情况压根不该出现,所以针对出现的异常情况进行补救的异常剪枝是一种很容易出错的行为,做为两手准备也就罢了,但第一次写成的代码必须能 ...

  8. lucene.net全文检索(一)相关概念及示例

    相关概念 站内搜索 站内搜索通俗来讲是一个网站或商城的"大门口",一般在形式上包括两个要件:搜索入口和搜索结果页面,但在其后台架构上是比较复杂的,其核心要件包括:中文分词技术.页面 ...

  9. 机器学习-无监督机器学习-密度聚类DBSCAN-19

    目录 1. DBSCAN 2. OPTICS 2. MeanShift 1. DBSCAN Density based clustering DBSCAN不要求我们指定cluster簇的数量,避免了异 ...

  10. [转帖]oceanbase 的简单介绍

    English | 中文版 OceanBase Database 是一个分布式关系型数据库.完全由蚂蚁集团自主研发. OceanBase 基于 Paxos 协议以及分布式架构,实现了高可用和线性扩展. ...