报道：深度强化学习实验室
资料来源：ICML2020
编辑：DeepRL
ICML 2020放榜了。入选论文创新高，共有1088篇论文突出重围。然而，接收率却是一年比一年低，这次仅为21.8%（去年为22.6%，前年为24.9%）。从整个榜单上看，谷歌仍为最强实力机构，共有138篇收录（数据包含谷歌大脑、DeepMind）。加州大学伯克利分校：88篇,斯坦福：75篇, MIT：66篇，微软：53篇，Facebook：32篇，IBM：19篇，其中国内机构也表现不俗。尤其是一直以来作为主力的大学们。清华：36篇，北大：20篇，上交：16篇，其中强化学习占有率达到了：11.58%， 下面是强化学习领域论文
(1) My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
Ilai Bistritz (Stanford University)，Tavor Z Baharav (Stanford University)，Amir Leshem (Bar-Ilan University)，Nicholas Bambos
(2) Generalization to New Actions in Reinforcement Learning
Ayush Jain (University of Southern California) · Andrew Szot (University of Southern California) · Joseph Lim (Univ. of Southern California)
(3) Generalized Neural Policies for Relational MDPs
Sankalp Garg (Indian Institute of Technology Delhi) · Aniket Bajpai (Indian Institute of Technology, Delhi) · Mausam (IIT Delhi)
(4) From Importance Sampling to Doubly Robust Policy Gradient
Jiawei Huang (University of Illinois at Urbana-Champaign) · Nan Jiang (University of Illinois at Urbana-Champaign)
(5) Kernel Methods for Cooperative Multi-Agent Learning with Delays
Abhimanyu Dubey (Massachusetts Institute of Technology) · Alex \`Sandy' Pentland (MIT)
(6) Robust Multi-Agent Decision-Making with Heavy-Tailed Payoffs
Abhimanyu Dubey (Massachusetts Institute of Technology) · Alex \`Sandy' Pentland (MIT)
(7) Learning the Valuations of a k-demand Agent
Hanrui Zhang (Duke University) · Vincent Conitzer (Duke)
(8) Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha (Indian Institute of Science (IISc), Bangalore) · Pierre Gaillard () · Michal Valko (DeepMind)
(9) Multi-Agent Determinantal Q-Learning
Yaodong Yang (Huawei Technology R&D UK) · Ying Wen (UCL) · Jun Wang (UCL) · Liheng Chen (Shanghai Jiao Tong University) · Kun Shao (Huawei Noah's Ark Lab) · David Mguni (Noah's Ark Laboratory, Huawei) · Weinan Zhang (Shanghai Jiao Tong University)
(10) Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara (Harvard University) · Jiawei Huang (University of Illinois at Urbana-Champaign) · Nan Jiang (University of Illinois at Urbana-Champaign)
(11) Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang (Nanyang Technological University) · Xu He (Nanyang Technological University) · Runsheng Yu (Nanyang Technological University) · Wei Qiu (Nanyang Technological University) · Bo An (Nanyang Technological University) · Zinovi Rabinovich (Nanyang Technological University)
(12) Multinomial Logit Bandit with Low Switching Cost
Kefan Dong (Tsinghua University) · Yingkai Li (Northwestern University) · Qin Zhang (Indiana University Bloomington) · Yuan Zhou (UIUC)
(13) Optimizing Data Usage via Differentiable Rewards
Xinyi Wang (Carnegie Mellon University) · Hieu Pham (Carnegie Mellon University) · Paul Michel (Carnegie Mellon University) · Antonios Anastasopoulos (Carnegie Mellon University) · Jaime Carbonell (Carnegie Mellon University) · Graham Neubig (Carnegie Mellon University)
(14) Optimistic Policy Optimization with Bandit Feedback
Lior Shani (Technion) · Yonathan Efroni (Technion) · Aviv Rosenberg (Tel Aviv University) · Shie Mannor (Technion)
(15) Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
Chi Jin (Princeton University) · Tiancheng Jin (University of Southern California) · Haipeng Luo (University of Southern California) · Suvrit Sra (MIT) · Tiancheng Yu (MIT)
(16) Asynchronous Coagent Networks
James Kostas (University of Massachusetts Amherst) · Chris Nota (University of Massachusetts Amherst) · Philip Thomas (University of Massachusetts Amherst)
(17) Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Yao Liu (Stanford University) · Pierre-Luc Bacon (Stanford University) · Emma Brunskill (Stanford University)
(18) Reinforcement Learning for Integer Programming: Learning to Cut
Yunhao Tang (Columbia University) · Shipra Agrawal (Columbia University) · Yuri Faenza (Columbia University)
(19) Safe Reinforcement Learning in Constrained Markov Decision Processes
Akifumi Wachi (IBM Research AI) · Yanan Sui (Tsinghua University)
(20) ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Tonghan Wang (Tsinghua University) · Heng Dong (Tsinghua) · Victor Lesser (UMASS) · Chongjie Zhang (Tsinghua University)
(21) Naive Exploration is Optimal for Online LQR
Max Simchowitz (UC Berkeley) · Dylan Foster (MIT)
(22) Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff (Oregon State University) · Qinxun Bai (Horizon Robotics) · Fuxin Li (Oregon State University) · Wei Xu (Horizon Robotics)
(23) Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Jie Xu (Massachusetts Institute of Technology) · Yunsheng Tian (Massachusetts Institute of Technology) · Pingchuan Ma (MIT) · Daniela Rus (MIT CSAIL) · Shinjiro Sueda (Texas A&M University) · Wojciech Matusik (MIT)
(24) Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
Nathan Kallus (Cornell University) · Masatoshi Uehara (Harvard University)
(25) Statistically Efficient Off-Policy Policy Gradients
Nathan Kallus (Cornell University) · Masatoshi Uehara (Harvard University)
(26) Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt (DeepMind) · Matteo Hessel (Deep Mind) · Karen Simonyan (DeepMind)
(27) Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Amin Rakhsha (MPI-SWS) · Goran Radanovic (Max Planck Institute for Software Systems) · Rati Devidze (Max Planck Institute for Software Systems) · Jerry Zhu (University of Wisconsin-Madison) · Adish Singla (Max Planck Institute (MPI-SWS))
(28) Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi (London School of Economics and Political Science) · Runzhe Wan (North Carolina State University) · Rui Song () · Wenbin Lu () · Ling Leng (Amazon)
(29) No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech (Facebook AI Research Paris & Inria Lille) · Evrard Garcelon (Facebook AI Research ) · Michal Valko (DeepMind) · Matteo Pirotta (Facebook AI Research) · Alessandro Lazaric (Facebook AI Research)
(30) OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Alexander Vezhnevets (DeepMind) · Yuhuai Wu (University of Toronto) · Maria Eckstein (UC Berkeley) · Rémi Leblond (DeepMind) · Joel Z Leibo (DeepMind)
(31) Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor Simm (Cambridge University) · Robert Pinsler (University of Cambridge) · Jose Hernandez-Lobato (University of Cambridge)
(32) ConQUR: Mitigating Delusional Bias in Deep Q-Learning
DiJia Su (Princeton University) · Jayden Ooi (Google) · Tyler Lu (Google) · Dale Schuurmans (Google / University of Alberta) · Craig Boutilier (Google)
(33) Provably Efficient Exploration in Policy Optimization
Qi Cai (Northwestern University) · Zhuoran Yang (Princeton University) · Chi Jin (Princeton University) · Zhaoran Wang (Northwestern U)
(34) Striving for simplicity and performance in off-policy DRL: Output Normalization and Non-Uniform Sampling
Che Wang (New York University) · Yanqiu Wu (New York University) · Quan Vuong (University of California San Diego) · Keith Ross (New York University Shanghai)
(35) Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Youzhi Zhang (Nanyang Technological University) · Bo An (Nanyang Technological University)
(36) Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Victor Campos (Barcelona Supercomputing Center) · Alexander Trott (Salesforce Research) · Caiming Xiong (Salesforce) · Richard Socher (Salesforce) · Xavier Giro-i-Nieto (Universitat Politecnica de Catalunya) · Jordi Torres (Barcelona Supercomputing Center)
(37) Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Brian Zhang (Carnegie Mellon University) · Tuomas Sandholm (Carnegie Mellon University)
(38) Extra-gradient with player sampling for faster convergence in n-player games
Samy Jelassi (Princeton University) · Carles Domingo-Enrich (NYU) · Damien Scieur (Samsung Advanced Institute of Technology AI Lab Montreal (SAIL)) · Arthur Mensch (ENS) · Joan Bruna (New York University)
(39) Entropy Minimization In Emergent Languages
Evgeny Kharitonov (FAIR) · Rahma Chaabouni (Facebook/ENS/INRIA) · Diane Bouchacourt (Facebook AI) · Marco Baroni (Facebook Artificial Intelligence Research)
(40) Discount Factor as a Regularizer in Reinforcement Learning
Ron Amit (Technion – Israel Institute of Technology) · Kamil Ciosek (Microsoft) · Ron Meir (Technion Israeli Institute of Technology)
(41) Domain Adaptive Imitation Learning
Kuno Kim (Stanford University) · Yihong Gu (Tsinghua University) · Jiaming Song (Stanford) · Shengjia Zhao (Stanford University) · Stefano Ermon (Stanford University)
(42) An Imitation Learning Approach for Cache Replacement
Evan Liu (Google) · Milad Hashemi (Google) · Kevin Swersky (Google Brain) · Parthasarathy Ranganathan (Google, USA) · Junwhan Ahn (Google)
(43) Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Lingxiao Wang (Northwestern University) · Zhuoran Yang (Princeton University) · Zhaoran Wang (Northwestern U)
(44) Multi-Agent Routing Value Iteration Network
Quinlan Sykora (Uber ATG) · Mengye Ren (Uber ATG / University of Toronto) · Raquel Urtasun (Uber ATG)
(45) A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu (University of California, Los Angeles) · Quanquan Gu (University of California, Los Angeles)
(46) Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Johannes Fischer (Karlsruhe Institute of Technology (KIT)) · Ömer Sahin Tas (Karlsruhe Institute of Technology (KIT))
(47) Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
Dylan Foster (MIT) · Alexander Rakhlin (MIT)
(48) Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits
Xi Liu (Texas A&M University) · Ping-Chun Hsieh (National Chiao Tung University) · Yu Heng Hung (NCTU) · Anirban Bhattacharya (Texas A&M University) · P. Kumar (Texas A&M University)
(49) Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su (Cornell University) · Pavithra Srinath (Microsoft Research) · Akshay Krishnamurthy (Microsoft Research)
(50) Linear bandits with Stochastic Delayed Feedback
Claire Vernade (DeepMind) · Alexandra Carpentier (Otto-von-Guericke University) · Tor Lattimore (DeepMind) · Giovanni Zappella (Amazon) · Beyza Ermis (Amazon Research) · Michael Brueckner (Amazon Research Berlin)
(51) Momentum-Based Policy Gradient Methods
Feihu Huang (University of Pittsburgh) · Shangqian Gao (University of Pittsburgh) · Jian Pei (Simon Fraser University) · Heng Huang (University of Pittsburgh)
(52) Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Alberto Maria Metelli (Politecnico di Milano) · Flavio Mazzolini (Politecnico di Milano) · Lorenzo Bisi (Politecnico di Milano) · Luca Sabbioni (Politecnico di Milano) · Marcello Restelli (Politecnico di Milano)
(53) What Can Learned Intrinsic Rewards Capture?
Zeyu Zheng (University of Michigan) · Junhyuk Oh (DeepMind) · Matteo Hessel (Deep Mind) · Zhongwen Xu (DeepMind) · Manuel Kroiss (DeepMind) · Hado van Hasselt (DeepMind) · David Silver (Google DeepMind) · Satinder Singh (DeepMind)
(54) Reinforcement Learning with Differential Privacy
Giuseppe Vietri (University of Minnesota) · Borja de Balle Pigem (Amazon Research) · Steven Wu (University of Minnesota) · Akshay Krishnamurthy (Microsoft Research)
(55) Improved Optimistic Algorithms for Logistic Bandits
Louis Faury (Criteo) · Marc Abeille (Criteo) · Clement Calauzenes (Criteo) · Olivier Fercoq (Telecom Paris)
(56) Growing Action Spaces
Gregory Farquhar (University of Oxford) · Laura Gustafson (Facebook AI Research) · Zeming Lin (Facebook AI Reseach) · Shimon Whiteson (Oxford University) · Nicolas Usunier (Facebook AI Research) · Gabriel Synnaeve (Facebook AI Research)
(57) Responsive Safety in Reinforcement Learning
Adam Stooke (UC Berkeley) · Joshua Achiam (OpenAI) · Pieter Abbeel (UC Berkeley & Covariant)
(58) Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto (Carnegie Mellon University) · Francis Song (DeepMind) · Jack Rae (DeepMind) · Razvan Pascanu (DeepMind) · Caglar Gulcehre (DeepMind) · Siddhant Jayakumar (DeepMind) · Max Jaderberg (DeepMind) · Raphael Lopez Kaufman (Deepmind) · Aidan Clark (DeepMind) · Seb Noury (DeepMind) · Matthew Botvinick (DeepMind) · Nicolas Heess (DeepMind) · Raia Hadsell (DeepMind)
(59) Learning to Score Behaviors for Guided Policy Optimization
Aldo Pacchiano (UC Berkeley) · Jack Parker-Holder (University of Oxford) · Yunhao Tang (Columbia University) · Krzysztof Choromanski (Google) · Anna Choromanska (NYU Tandon School of Engineering) · Michael Jordan (UC Berkeley)
(60) Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou (UCLA) · Lihong Li (Google Research) · Quanquan Gu (University of California, Los Angeles)
(61) Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Nian Si (Stanford University) · Fan Zhang (Stanford University) · Zhengyuan Zhou (Stanford University) · Jose Blanchet (Stanford University)
(62) Efficient Policy Learning from Surrogate-Loss Classification Reductions
Andrew Bennett (Cornell University) · Nathan Kallus (Cornell University)
(63) Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar (Facebook AI Research) · Abhinav Gupta (Carnegie Mellon University)
(64) Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe (OpenAI) · Chris Hesse (OpenAI) · Jacob Hilton (OpenAI) · John Schulman (OpenAI)
(65) What can I do here? A Theory of Affordances in Reinforcement Learning
Khimya Khetarpal (McGill University, Mila Montreal) · Zafarali Ahmed (DeepMind) · Gheorghe Comanici (DeepMind) · David Abel (Brown University) · Doina Precup (DeepMind)
(66) Data Valuation using Reinforcement Learning
Jinsung Yoon (University of California, Los Angeles) · Sercan O. Arik (Google) · Tomas Pfister (Google)
(67) Reward-Free Exploration for Reinforcement Learning
Chi Jin (Princeton University) · Akshay Krishnamurthy (Microsoft Research) · Max Simchowitz (UC Berkeley) · Tiancheng Yu (MIT )
(68) Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
Junzhe Zhang (Columbia University)
(69) Lookahead-Bounded Q-learning
Ibrahim El Shar (University of Pittsburgh) · Daniel Jiang (University of Pittsburgh)
(70) Evaluating the Performance of Reinforcement Learning Algorithms
Scott Jordan (University of Massachusetts Amherst) · Yash Chandak (University of Massachusetts Amherst) · Daniel Cohen (University of Massachusetts Amherst) · Mengxue Zhang (umass Amherst ) · Philip Thomas (University of Massachusetts Amherst)
(71) Provable Self-Play Algorithms for Competitive Reinforcement Learning
Yu Bai (Salesforce Research) · Chi Jin (Princeton University)
(72) A Game Theoretic Perspective on Model-Based Reinforcement Learning
Aravind Rajeswaran (University of Washington) · Igor Mordatch (OpenAI) · Vikash Kumar (Google)
(73) Optimizing for the Future in Non-Stationary MDPs
Yash Chandak (University of Massachusetts Amherst) · Georgios Theocharous (Adobe Research) · Shiv Shankar (University of Massachusetts) · Martha White (University of Alberta) · Sridhar Mahadevan (Adobe Research) · Philip Thomas (University of Massachusetts Amherst)
(74) Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Tung-Che Liang (Duke University) · Zhanwei Zhong (Duke University) · Yaas Bigdeli (Duke Univsersity) · Tsung-Yi Ho (National Tsing Hua University) · Richard Fair (Duke University) · Krishnendu Chakrabarty (Duke University)
(75) Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Aleksei Petrenko (University of Southern California) · Zhehui Huang (University of Southern California) · Tushar Kumar (University of Southern California) · Gaurav Sukhatme (University of Southern California) · Vladlen Koltun (Intel Labs)
(76) Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang (Tianjin University) · Jianye Hao (Tianjin University) · Guangyong Chen (Tencent) · Hongyao Tang (Tianjin University) · Yingfeng Chen (NetEase Fuxi AI Lab) · Yujing Hu (NetEase Fuxi AI Lab) · Changjie Fan (Netease) · Zhongyu Wei (Fudan University)
(77) Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Tianyi Lin (UC Berkeley) · Zhengyuan Zhou (Stanford University) · Panayotis Mertikopoulos (CNRS) · Michael Jordan (UC Berkeley)
(78) When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Feng Zhu (Peking University) · Zeyu Zheng (UC Berkeley)
(79) Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee (UC Berkeley) · Younggyo Seo (KAIST) · Seunghyun Lee (KAIST) · Honglak Lee (Google / U. Michigan) · Jinwoo Shin (KAIST)
(80) Structured Policy Iteration for Linear Quadratic Regulator
Youngsuk Park (Stanford University) · Ryan Rossi (Adobe Research) · Zheng Wen (DeepMind) · Gang Wu (Adobe Research) · Handong Zhao (Adobe Research)
(81) Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill (DeepMind) · Florent Altché (DeepMind) · Yunhao Tang (Columbia University) · Thomas Hubert (DeepMind) · Michal Valko (DeepMind) · Ioannis Antonoglou (Deepmind) · Remi Munos (DeepMind)
(82) On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong (Tsinghua University) · Yuping Luo (Princeton University) · Tianhe Yu (Stanford University) · Chelsea Finn (Stanford) · Tengyu Ma (Stanford)
(83) Intrinsic Reward Driven Imitation Learning via Generative Model
Xingrui Yu (University of Technology Sydney) · Yueming LYU (University of Technology Sydney) · Ivor Tsang (University of Technology Sydney)
(84) Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota (Mitsubishi Electric Corporation) · Tomoaki Oiki (Mitsubishi Electric) · Devesh Jha (Mitsubishi Electric Research Labs) · Toshisada Mariyama (Mitsubishi Electric) · Daniel Nikovski (Mitsubishi Electric Research Labs)
(85) Batch Reinforcement Learning with Hyperparameter Gradients
Byung-Jun Lee (KAIST) · Jongmin Lee (KAIST) · Peter Vrancx (PROWLER.io) · Dongho Kim (Prowler.io) · Kee-Eung Kim (KAIST)
(86) Sub-Goal Trees--a Framework for Goal-Based Reinforcement Learning
Tom Jurgenson (Technion) · Or Avner (Technion) · Edward Groshev (Osaro, Inc.) · Aviv Tamar (Technion)
(87) Agent57: Outperforming the Atari Human Benchmark
Adrià Puigdomenech Badia (Deepmind) · Bilal Piot (DeepMind) · Steven Kapturowski (Deepmind) · Pablo Sprechmann (Google DeepMind) · Oleksandr Vitvitskyi (DeepMind) · Zhaohan Guo (DeepMind) · Charles Blundell (DeepMind)
(88) Stochastically Dominant Distributional Reinforcement Learning
John Martin (Stevens Institute of Technology) · Michal Lyskawinski (Stevens Institute of Technology) · Xiaohu Li (Stevens Institute of Technology) · Brendan Englot (Stevens Institute of Technology)
(89) Gradient-free Online Learning in Continuous Games with Delayed Rewards
Amélie Héliou (Criteo) · Panayotis Mertikopoulos (CNRS) · Zhengyuan Zhou (Stanford University)
(90) Fast Adaptation to New Environments via Policy-Dynamics Value Functions
Roberta Raileanu (NYU) · Max Goldstein (NYU) · Arthur Szlam (Facebook) · Facebook Rob Fergus (Facebook AI Research, NYU)
(91) A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
Salman Sadiq Shuvo (University of South Florida) · Yasin Yilmaz (University of South Florida) · Alan Bush (University of South Florida) · Mark Hafen (University of South Florida)
(92) Fast computation of Nash Equilibria in Imperfect Information Games
Remi Munos (DeepMind) · Julien Perolat (DeepMind) · Jean-Baptiste Lespiau (DeepMind) · Mark Rowland (DeepMind) · Bart De Vylder (DeepMind) · Marc Lanctot (DeepMind) · Finbarr Timbers (DeepMind) · Daniel Hennes (DeepMind) · Shayegan Omidshafiei (DeepMind) · Audrunas Gruslys (DeepMind) · Mohammad Gheshlaghi Azar (Deepmind) · Edward Lockhart (DeepMind) · Karl Tuyls (DeepMind)
(93) Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Daniel Jarrett (University of Cambridge) · Mihaela van der Schaar (University of Cambridge)
(94) Tightening Exploration in Upper Confidence Reinforcement Learning
Hippolyte Bourel (ENS Rennes) · Odalric-Ambrym Maillard (Inria Lille - Nord Europe) · Mohammad Sadegh Talebi (University of Copenhagen)
(95) Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Zhaohan Guo (DeepMind) · Bernardo Avila Pires (DeepMind) · Mohammad Gheshlaghi Azar (Deepmind) · Bilal Piot (DeepMind) · Florent Altché (DeepMind) · Jean-Bastien Grill (DeepMind) · Remi Munos (DeepMind)
(96) Invariant Causal Prediction for Block MDPs
Clare Lyle (University of Oxford) · Amy Zhang (McGill University) · Angelos Filos (University of Oxford) · Shagun Sodhani (Facebook AI Research) · Marta Kwiatkowska (Oxford University) · Yarin Gal (University of Oxford) · Doina Precup (McGill University / DeepMind) · Joelle Pineau (McGill University / Facebook)
(97) Deep Reinforcement Learning with Smooth Policy
Qianli Shen (Peking University) · Yan Li (Georgia Tech) · Haoming Jiang (Georgia Tech) · Zhaoran Wang (Northwestern) · Tuo Zhao (Gatech)
(98) Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei (University of Southern California) · Mehdi Jafarnia (University of Southern California) · Haipeng Luo (University of Southern California) · Hiteshi Sharma (University of Southern California) · Rahul Jain (USC)
(99) Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra (Microsoft) · Mikael Henaff (Microsoft) · Akshay Krishnamurthy (Microsoft Research) · John Langford (Microsoft Research)
(100) Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Yaqi Duan (Princeton University) · Zeyu Jia (Peking University) · Mengdi Wang (Princeton University)
(101) Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang (Uber AI) · Joel Lehman () · Aditya Rawal (Uber AI Labs) · Jiale Zhi (Uber AI) · Yulun Li (Uber AI) · Jeffrey Clune (Open AI) · Kenneth Stanley (Uber AI and University of Central Florida)
(102) Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang (UW-Madison) · Yuzhe Ma (Univ. of Wisconsin-Madison) · Adish Singla (Max Planck Institute (MPI-SWS)) · Jerry Zhu (University of Wisconsin-Madison)
(103) Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar (MIT) · Fredrik Johansson (Chalmers University of Technology) · John Guttag (MIT) · David Sontag (Massachusetts Institute of Technology)
(104) Provably Efficient Model-based Policy Adaptation
Yuda Song (University of California, San Diego) · Aditi Mavalankar (University of California San Diego) · Wen Sun (Microsoft Research) · Sicun Gao (University of California, San Diego)
(105) Stochastic Regret Minimization in Extensive-Form Games
Gabriele Farina (Carnegie Mellon University) · Christian Kroer (Columbia University) · Tuomas Sandholm (Carnegie Mellon University)
(106) Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis (University of Toronto) · Harris Chan (University of Toronto, Vector Institute) · Stephen Zhao (University of Toronto) · Bradly Stadie (Vector Institute) · Jimmy Ba (University of Toronto)
(107) Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Jesse Zhang (UC Berkeley) · Brian Cheung (UC Berkeley) · Chelsea Finn (Stanford) · Sergey Levine (UC Berkeley) · Dinesh Jayaraman (University of Pennsylvania)
(108) An Optimistic Perspective on Offline Deep Reinforcement Learning
Rishabh Agarwal (Google Research, Brain Team) · Dale Schuurmans (Google / University of Alberta) · Mohammad Norouzi (Google Brain)
(109) Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Gellért Weisz (DeepMind) · Tor Lattimore (DeepMind) · Csaba Szepesvari (DeepMind/University of Alberta)
(110) Representations for Stable Off-Policy Reinforcement Learning
Dibya Ghosh (Google) · Marc Bellemare (Google Brain)
(111) Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics
Yihao Feng (The University of Texas at Austin) · Tongzheng Ren (UT Austin) · Ziyang Tang (University of Texas at Austin) · Qiang Liu (UT Austin)
(112) Multi-Step Greedy Reinforcement Learning Algorithms
Manan Tomar (Indian Institute of Technology, Madras) · Yonathan Efroni (Technion) · Mohammad Ghavamzadeh (Facebook AI Research)
(113) On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei (Google / University of Alberta) · Chenjun Xiao (Google / University of Alberta) · Csaba Szepesvari (DeepMind/University of Alberta) · Dale Schuurmans (University of Alberta)
(114) Estimating Q(s,s') with Deterministic Dynamics Gradients
Ashley Edwards (Uber AI) · Himanshu Sahni (Georgia Institute of Technology) · Rosanne Liu (Deep Collective) · Jane Hung (Uber) · Ankit Jain (Uber AI Labs) · Rui Wang (Uber AI) · Adrien Ecoffet (Uber AI) · Thomas Miconi (Uber AI Labs) · Charles Isbell (Georgia Institute of Technology) · Jason Yosinski (Uber Labs)
(115) Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman (Harvard University) · Joseph Futoma (Harvard University) · Yao Liu (Stanford University) · Sonali Parbhoo (Harvard University) · Leo Celi (MIT) · Emma Brunskill (Stanford University) · Finale Doshi-Velez (Harvard University)
(116) CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning
Michael Laskin (UC Berkeley) · Pieter Abbeel (UC Berkeley & Covariant) · Aravind Srinivas (UC Berkeley)
(117) Generative Pretraining From Pixels
Mark Chen (OpenAI) · Alec Radford (OpenAI) · Rewon Child (OpenAI) · Jeffrey K Wu (OpenAI) · Heewoo Jun (OpenAI) · David Luan (OpenAI) · Ilya Sutskever (OpenAI)
(118) R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Zhongxiang Dai (National University of Singapore) · Yizhou Chen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Patrick Jaillet (MIT) · Teck-Hua Ho (National University of Singapore)
(119) Revisiting Fundamentals of Experience Replay
William Fedus (University of Montreal/Google Brain) · Prajit Ramachandran (Google) · Rishabh Agarwal (Google Research, Brain Team) · Yoshua Bengio (Mila / U. Montreal) · Hugo Larochelle (Google Brain) · Mark Rowland (DeepMind) · Will Dabney (DeepMind)
(120) Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam Elmachtoub (Columbia University) · Jason Cheuk Nam Liang (MIT) · Ryan McNellis (Amazon)
(121) Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati (99andBeyond) · Boris Sattarov (99andBeyond) · Sufeng Niu (Linkedin) · Haoran Wei (University of Delaware) · Yashaswi Pathak (International Institute of Information Technology,Hyderabad) · Shengchao Liu (MILA-UdeM) · Shengchao Liu (Mila, Université de Montréal) · Simon Blackburn (Mila) · Karam Thomas (99andBeyond) · Connor Coley (MIT) · Jian Tang (HEC Montreal & MILA) · Sarath Chandar (Mila / École Polytechnique de Montréal) · Yoshua Bengio (Mila / U. Montreal)
(122) Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis (Rice University) · Minjian Xin (Shanghai Jiao Tong University) · Dilip Arumugam (Stanford University) · Kevin Feigelis (Stanford University) · Daniel Yamins (Stanford University)
(123) Predictive Coding for Locally-Linear Control
Rui Shu (Stanford University) · Tung Nguyen (VinAI Research) · Yinlam Chow (Google) · Tuan Pham (VinAI) · Khoat Than (VinAI & HUST) · Mohammad Ghavamzadeh (Facebook) · Stefano Ermon (Stanford University) · Hung Bui (VinAI Research)
(124) Bidirectional Model-based Policy Optimization
Hang Lai (Shanghai Jiao Tong University) · Jian Shen (Shanghai Jiao Tong University) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong University)
(125) Efficiently Solving MDPs with Stochastic Mirror Descent
Yujia Jin (Stanford University) · Aaron Sidford (Stanford)
(126) A distributional view on multi objective policy optimization
Abbas Abdolmaleki (Google DeepMind) · Sandy Huang (DeepMind) · Leonard Hasenclever (DeepMind) · Michael Neunert (Google DeepMind) · Martina Zambelli (DeepMind) · Murilo Martins (DeepMind) · Francis Song (DeepMind) · Nicolas Heess (DeepMind) · Raia Hadsell (DeepMind) · Martin Riedmiller (DeepMind)
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