必看！52篇深度强化学习收录论文汇总 | AAAI 2020
The following article comes from 深度强化学习实验室 Author DeepRL
来源 | 深度强化学习实验室（ID:Deep-RL）
作者 | DeepRL
AAAI 2020 共收到的有效论文投稿超过 8800 篇，其中 7737 篇论文进入评审环节，最终收录数量为 1591 篇，收录率为 20.6%，而被接受论文列表中强化学习有52+篇，录取比约为3%，其中接收论文中就单位而言：Google Brain, DeepMind, Tsinghua University,UCL,Tencent AI Lab,Peking University, IBM, FaceBook等被录取一大片，就作者而言，不但有强化学习老爷子Sutton的文章(第48篇)，也有后起之秀等。
论文涉及了环境、理论算法、应用以及多智能体等各个方向。以下是详细列表：
[1]. Google Research Football: A Novel Reinforcement Learning Environment
Karol Kurach (Google Brain)*; Anton Raichuk (Google); Piotr Stańczyk (Google Brain); Michał Zając (Google Brain); Olivier Bachem (Google Brain); Lasse Espeholt (DeepMind); Carlos Riquelme (Google Brain); Damien Vincent (Google Brain); Marcin Michalski (Google); Olivier Bousquet (Google); Sylvain Gelly (Google Brain)
[2]. Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
Xiaojian Ma (University of California, Los Angeles)*; Mingxuan Jing (Tsinghua University); Wenbing Huang (Tsinghua University); Chao Yang (Tsinghua University); Fuchun Sun (Tsinghua); Huaping Liu (Tsinghua University); Bin Fang (Tsinghua University)
[3]. Proximal Distilled Evolutionary Reinforcement Learning
Cristian Bodnar (University of Cambridge)*; Ben Day (University of Cambridge); Pietro Lió (University of Cambridge)
[4]. Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video
Jie Wu (Sun Yat-sen University)*; Guanbin Li (Sun Yat-sen University); si liu (Beihang University); Liang Lin (DarkMatter AI)
[5]. RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
Nan Jiang (Tsinghua University)*; Sheng Jin (Tsinghua University); Zhiyao Duan (Unversity of Rochester); Changshui Zhang (Tsinghua University)
[6]. Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
Deheng Ye (Tencent)*; Zhao Liu (Tencent); Mingfei Sun (Tencent); Bei Shi (Tencent AI Lab); Peilin Zhao (Tencent AI Lab); Hao Wu (Tencent); Hongsheng Yu (Tencent); Shaojie Yang (Tencent); Xipeng Wu (Tencent); Qingwei Guo (Tsinghua University); Qiaobo Chen (Tencent); Yinyuting Yin (Tencent); Hao Zhang (Tencent); Tengfei Shi (Tencent); Liang Wang (Tencent); Qiang Fu (Tencent AI Lab); Wei Yang (Tencent AI Lab); Lanxiao Huang (Tencent)
[7]. Partner Selection for the Emergence of Cooperation in Multi‐Agent Systems using Reinforcement Learning
Nicolas Anastassacos (The Alan Turing Institute)*; Steve Hailes (University College London); Mirco Musolesi (UCL)
[8]. Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents
Felipe Leno da Silva (University of Sao Paulo)*; Pablo Hernandez-Leal (Borealis AI); Bilal Kartal (Borealis AI); Matthew Taylor (Borealis AI)
[9]. MetaLight: Value-based Meta-reinforcement Learning for Traffic Signal Control
Xinshi Zang (Shanghai Jiao Tong University)*; Huaxiu Yao (Pennsylvania State University); Guanjie Zheng (Pennsylvania State University); Nan Xu (University of Southern California); Kai Xu (Shanghai Tianrang Intelligent Technology Co., Ltd); Zhenhui (Jessie) Li (Penn State University)
[10].Adaptive Quantitative Trading: an Imitative Deep Reinforcement Learning Approach
Yang Liu (University of Science and Technology of China)*; Qi Liu (" University of Science and Technology of China, China"); Hongke Zhao (Tianjin University); Zhen Pan (University of Science and Technology of China); Chuanren Liu (The University of Tennessee Knoxville)
[11]. Neighborhood Cognition Consistent Multi‐Agent Reinforcement Learning
Hangyu Mao (Peking University)*; Wulong Liu (Huawei Noah's Ark Lab); Jianye Hao (Tianjin University); Jun Luo (Huawei Technologies Canada Co. Ltd.); Dong Li ( Huawei Noah's Ark Lab); Zhengchao Zhang (Peking University); Jun Wang (UCL); Zhen Xiao (Peking University)
[12]. SMIX(): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning
Chao Wen (Nanjing University of Aeronautics and Astronautics)*; Xinghu Yao (Nanjing University of Aeronautics and Astronautics); Yuhui Wang (Nanjing University of Aeronautics and Astronautics, China); Xiaoyang Tan (Nanjing University of Aeronautics and Astronautics, China)
[13]. Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
Satoshi Kosugi (The University of Tokyo)*; Toshihiko Yamasaki (The University of Tokyo)
[14]. Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach
Jun Wang (University of Science and Technology of China)*; Hefu Zhang (University of Science and Technology of China); Qi Liu (" University of Science and Technology of China, China"); Zhen Pan (University of Science and Technology of China); Hanqing Tao (University of Science and Technology of China (USTC))
[15]. Model and Reinforcement Learning for Markov Games with Risk Preferences
Wenjie Huang (Shenzhen Research Institute of Big Data)*; Hai Pham Viet (Department of Computer Science, School of Computing, National University of Singapore); William Benjamin Haskell (Supply Chain and Operations Management Area, Krannert School of Management, Purdue University)
[16]. Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning
Liang Tong (Washington University in Saint Louis)*; Aron Laszka (University of Houston); Chao Yan (Vanderbilt UNIVERSITY); Ning Zhang (Washington University in St. Louis); Yevgeniy Vorobeychik (Washington University in St. Louis)
[17]. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large‐Scale Traffic Signal Control
Chacha Chen (Pennsylvania State University)*; Hua Wei (Pennsylvania State University); Nan Xu (University of Southern California); Guanjie Zheng (Pennsylvania State University); Ming Yang (Shanghai Tianrang Intelligent Technology Co., Ltd); Yuanhao Xiong (Zhejiang University); Kai Xu (Shanghai Tianrang Intelligent Technology Co., Ltd); Zhenhui (Jessie) Li (Penn State University)
[18]. Deep Reinforcement Learning for Active Human Pose Estimation
Erik Gärtner (Lund University)*; Aleksis Pirinen (Lund University); Cristian Sminchisescu (Lund University)
[19]. Be Relevant, Non‐redundant, Timely: Deep Reinforcement Learning for Real‐time Event Summarization
Min Yang ( Chinese Academy of Sciences)*; Chengming Li (Chinese Academy of Sciences); Fei Sun (Alibaba Group); Zhou Zhao (Zhejiang University); Ying Shen (Peking University Shenzhen Graduate School); Chenglin Wu (fuzhi.ai)
[20]. A Tale of Two‐Timescale Reinforcement Learning with the Tightest Finite‐Time Bound
Gal Dalal (Technion)*; Balazs Szorenyi (Yahoo Research); Gugan Thoppe (Duke University)
[21]. Reinforcement Learning with Perturbed Rewards
Jingkang Wang (University of Toronto); Yang Liu (UCSC); Bo Li (University of Illinois at Urbana–Champaign)*
[22]. Exploratory Combinatorial Optimization with Reinforcement Learning
Thomas Barrett (University of Oxford)*; William Clements (Unchartech); Jakob Foerster (Facebook AI Research); Alexander Lvovsky (Oxford University)
[23]. Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain (Mila, McGill University)*; Liam Fedus (Google); Hugo Larochelle (Google); Doina Precup (McGill University); Marc G. Bellemare (Google Brain)
[24]. Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Xian Yeow Lee (Iowa State University)*; Sambit Ghadai (Iowa State University); Kai Liang Tan (Iowa State University); Chinmay Hegde (New York University); Soumik Sarkar (Iowa State University)
[25]. Modelling Sentence Pairs via Reinforcement Learning: An Actor‐Critic Approach to Learn the Irrelevant Words
MAHTAB AHMED (The University of Western Ontario)*; Robert Mercer (The University of Western Ontario)
[26]. Transfer Reinforcement Learning using Output-Gated Working Memory
Arthur Williams (Middle Tennessee State University)*; Joshua Phillips (Middle Tennessee State University)
[27]. Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Yunan Ye (Zhejiang University)*; Hengzhi Pei (Fudan University); Boxin Wang (University of Illinois at Urbana- Champaign); Pin-Yu Chen (IBM Research); Yada Zhu (IBM Research); Jun Xiao (Zhejiang University); Bo Li (University of Illinois at Urbana–Champaign)
[28]. Deep Reinforcement Learning for General Game Playing
Adrian Goldwaser (University of New South Wales)*; Michael Thielscher (University of New South Wales)
[29]. Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
Jianwen Sun (Nanyang Technological University)*; Tianwei Zhang ( Nanyang Technological University); Xiaofei Xie (Nanyang Technological University); Lei Ma (Kyushu University); Yan Zheng (Tianjin University); Kangjie Chen (Tianjin University); Yang Liu (Nanyang Technology University, Singapore)
[30]. LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games
Leonard Adolphs (ETHZ)*; Thomas Hofmann (ETH Zurich)
[31]. Induction of Subgoal Automata for Reinforcement Learning
Daniel Furelos-Blanco (Imperial College London)*; Mark Law (Imperial College London); Alessandra Russo (Imperial College London); Krysia Broda (Imperial College London); Anders Jonsson (UPF)
[32]. MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
wentian li (Tsinghua University)*; XIDONG FENG (department of Automation,Tsinghua University); Haotian An (Tsinghua University); Xiang Yao Ng (Tsinghua University); Yu-Jin Zhang (Tsinghua University)
[33]. Explainable Reinforcement Learning Through a Causal Lens
Prashan Madumal (University of Melbourne)*; Tim Miller (University of Melbourne); Liz Sonenberg (University of Melbourne); Frank Vetere (University of Melbourne)
[34]. Reinforcement Learning based Metapath Discovery in Large-scale Heterogeneous Information Networks
Guojia Wan (Wuhan University); Bo Du (School of Compuer Science, Wuhan University)*; Shirui Pan (Monash University); Reza Haffari (Monash University, Australia)
[35]. Reinforcement Learning When All Actions are Not Always Available
Yash Chandak (University of Massachusetts Amherst)*; Georgios Theocharous ("Adobe Research, USA"); Blossom Metevier (University of Massachusetts, Amherst); Philip Thomas (University of Massachusetts Amherst)
[36]. Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions
Weiran Shen (Carnegie Mellon University)*; Binghui Peng (Columbia University); Hanpeng Liu (Tsinghua University); Michael Zhang (Chinese University of Hong Kong); Ruohan Qian (Baidu Inc.); Yan Hong (Baidu Inc.); Zhi Guo (Baidu Inc.); Zongyao Ding (Baidu Inc.); Pengjun Lu (Baidu Inc.); Pingzhong Tang (Tsinghua University)
[37]. Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning
Rich Contextual Representations Aditya Modi (Univ. of Michigan Ann Arbor)*; Debadeepta Dey (Microsoft); Alekh Agarwal (Microsoft); Adith Swaminathan (Microsoft Research); Besmira Nushi (Microsoft Research); Sean Andrist (Microsoft Research); Eric Horvitz (MSR)
[38]. Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model
Ya Xiao (Tongji University)*; Chengxiang Tan (Tongji University); Zhijie Fan (The Third Research Institute of the Ministry of Public Security); Qian Xu (Tongji University); Wenye Zhu (Tongji University)
[39]. Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes
Tomas Brazdil (Masaryk University); Krishnendu Chatterjee (IST Austria); Petr Novotný (Masaryk University)*; Jiří Vahala (Masaryk University)
[40]. Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
Qi Zhou (University of Science and Technology of China); Houqiang Li (University of Science and Technology of China); Jie Wang (University of Science and Technology of China)*
[41]. Reinforcement Learning with Non-Markovian Rewards
Maor Gaon (Ben-Gurion University); Ronen Brafman (BGU)*
[42]. Modular Robot Design Synthesis with Deep Reinforcement Learning
Julian Whitman (Carnegie Mellon University)*; Raunaq Bhirangi (Carnegie Mellon University); Matthew Travers (CMU); Howie Choset (Carnegie Melon University)
[42]. BAR -A Reinforcement Learning Agent for Bounding-Box Automated Refinement
Morgane Ayle (American University of Beirut - AUB)*; Jimmy Tekli (BMW Group / Université de Franche-Comté - UFC); Julia Zini (American University of Beirut - AUB); Boulos El Asmar (BMW Group / Karlsruher Institut für Technologie - KIT); Mariette Awad (American University of Beirut- AUB)
[44]. Hierarchical Reinforcement Learning for Open-Domain Dialog
Abdelrhman Saleh (Harvard University)*; Natasha Jaques (MIT); Asma Ghandeharioun (MIT); Judy Hanwen Shen(MIT); Rosalind Picard (MIT Media Lab)
[45]. Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning
Liqiang Xiao (Artificial Intelligence Institute, SJTU)*; Lu Wang (Khoury College of Computer Science, Northeastern University); Hao He (Shanghai Jiao Tong University); Yaohui Jin (Artificial Intelligence Institute, SJTU)
[46]. Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning
Xiang Ni (IBM Research); Jing Li (NJIT); Wang Zhou (IBM Research); Mo Yu (IBM T. J. Watson)*; Kun-Lung Wu (IBM Research)
[47]. Actor Critic Deep Reinforcement Learning for Neural Malware Control
Yu Wang (Microsoft)*; Jack Stokes (Microsoft Research); Mady Marinescu (Microsoft Corporation)
[48]. Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning
Kristopher De Asis (University of Alberta)*; Alan Chan (University of Alberta); Silviu Pitis (University of Toronto); Richard Sutton (University of Alberta); Daniel Graves (Huawei)
[49]. Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning
Liqun Chen (Duke University)*; Ke Bai (Duke University); Chenyang Tao (Duke University); Yizhe Zhang (Microsoft Research); Guoyin Wang (Duke University); Wenlin Wang (Duke Univeristy); Ricardo Henao (Duke University); Lawrence Carin Duke (CS)
[50]. Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Fabio Pardo (Imperial College London)*; Vitaly Levdik (Imperial College London); Petar Kormushev (Imperial College London)
[51]. Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning
Tian Tan (Stanford University)*; Zhihan Xiong (Stanford University); Vikranth Dwaracherla (Stanford University)
[52]. Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning
Sanket Shah (Singpore Management University)*; Arunesh Sinha (Singapore Management University); Pradeep Varakantham (Singapore Management University); Andrew Perrault (Harvard University); Milind Tambe (Harvard University)
关于论文的详细解读请查看Github：
https://github.com/NeuronDance/DeepRL/tree/master/DRL-ConferencePaper/AAAI/2020
（*本文为AI科技大本营转载文章，转载请微信联系1092722531）
◆
精彩推荐
◆
2020年，由 CSDN 主办的「Python开发者日」活动（Python Day）正式启动。我们将与 PyCon 官方授权的 PyCon中国社区合作，联手顶尖企业、行业与技术专家，通过精彩的技术干货内容、有趣多元化的活动等诸多体验，共同为中国 IT 技术开发者搭建专业、开放的技术交流与成长的家园。未来，我们和中国万千开发者一起分享技术、践行技术，铸就中国原创技术力量。
CSDN「Python Day」咨询群 🔽来~一起聊聊Python
如果群满100人，无法自动进入，可添加会议小助手微信：婷婷，151 0101 4297（电话同微信）
推荐阅读