ICML 是机器学习领域最重要的会议之一，在该会议上发表论文的研究者也会备受关注。近年来，ICML会议的投稿数量一直增长：ICML 2020 投稿量为4990篇，ICML 2021的投稿量5513，在一个月之前，ICML 2021的论文接收结果已经公布，其中1184篇论文被接收，接收率为 21.5% 。而就在近日，ICML 2021的论文接收列表也终于放了出来，本文整理强化学习领域大约163篇文章，具体列表如下：

1. Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research

Johan Obando Ceron (UAO) · Pablo Samuel Castro (Google Brain)

2. First-Order Methods for Wasserstein Distributionally Robust MDP

Julien Grand-Clement (IEOR Department, Columbia University) · Christian Kroer (Columbia University)

3. REPAINT: Knowledge Transfer in Deep Reinforcement Learning

Yunzhe Tao (ByteDance) · Sahika Genc (Amazon AI) · Jonathan Chung (AWS) · TAO SUN (Amazon.com) · Sunil Mallya (Amazon AWS)

4. Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

Yue Wu (Carnegie Mellon University) · Shuangfei Zhai (Apple) · Nitish Srivastava (Apple) · Joshua Susskind (Apple, Inc.) · Jian Zhang (Apple Inc.) · Ruslan Salakhutdinov (Carnegie Mellen University) · Hanlin Goh (Apple)

5. Detecting Rewards Deterioration in Episodic Reinforcement Learning

Ido Greenberg (Technion) · Shie Mannor (Technion)

6. Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity

Zhang Zihan (Tsinghua University) · Yuan Zhou (UIUC) · Xiangyang Ji (Tsinghua University)

7. Near Optimal Reward-Free Reinforcement Learning

Zhang Zihan (Tsinghua University) · Simon Du (University of Washington) · Xiangyang Ji (Tsinghua University)

8. On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP

Tianhao Wu (Peking University) · Yunchang Yang (Center for Data Science, Peking University) · Simon Du (University of Washington) · Liwei Wang (Peking University)

9. Average-Reward Off-Policy Policy Evaluation with Function Approximation

Shangtong Zhang (University of Oxford) · Yi Wan (University of Alberta) · Richard Sutton (DeepMind / Univ Alberta) · Shimon Whiteson (University of Oxford)

10. Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL

Andrea Zanette (Stanford University)

11. Is Model-Free Learning Nearly Optimal for Non-Stationary RL?

Weichao Mao (University of Illinois at Urbana-Champaign) · Kaiqing Zhang (University of Illinois at Urbana-Champaign/MIT) · Ruihao Zhu (MIT) · David Simchi-Levi (MIT) · Tamer Basar (University of Illinois at Urbana-Champaign)

12. DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Daochen Zha (Texas A&M University) · Jingru Xie (Kwai Inc.) · Wenye Ma (Kuaishou) · Sheng Zhang (Georgia Institute of Technology) · Xiangru Lian (Kwai Inc.) · Xia Hu (Texas A&M University) · Ji Liu (Kwai Seattle AI lab, University of Rochester)

13. Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies

Jimmy (Tsung-Yen) Yang (Princeton University) · Justinian Rosca (Siemens Corp.) · Karthik Narasimhan (Princeton) · Peter Ramadge (Princeton)

14. Revisiting Peng's Q($\lambda$) for Modern Reinforcement Learning

Tadashi Kozuno (University of Alberta) · Yunhao Tang (Columbia University) · Mark Rowland (DeepMind) · Remi Munos (DeepMind) · Steven Kapturowski (Deepmind) · Will Dabney (DeepMind) · Michal Valko (DeepMind / Inria / ENS Paris-Saclay) · David Abel (DeepMind)

15. Ensemble Bootstrapping for Q-Learning

Oren Peer (Technion) · Chen Tessler (Technion) · Nadav Merlis (Technion) · Ron Meir (Technion Israeli Institute of Technology)

16. Phasic Policy Gradient

Karl Cobbe (OpenAI) · Jacob Hilton (OpenAI) · Oleg Klimov (OpenAI) · John Schulman (OpenAI)

17. Optimal Off-Policy Evaluation from Multiple Logging Policies

Nathan Kallus (Cornell University) · Yuta Saito (Tokyo Institute of Technology.) · Masatoshi Uehara (Cornell University)

18. Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning

Yaqi Duan (Princeton University) · Chi Jin (Princeton University) · Zhiyuan Li (Princeton University)

19. Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm

sajad khodadadian (georgia institute of technology) · Zaiwei Chen (Georgia Institute of Technology) · Siva Maguluri (Georgia Tech)

20. SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Kimin Lee (UC Berkeley) · Michael Laskin (UC Berkeley) · Aravind Srinivas (UC Berkeley) · Pieter Abbeel (UC Berkeley & Covariant)

21. Reinforcement Learning with Prototypical Representations

Denis Yarats (New York University) · Rob Fergus (Facebook / NYU) · Alessandro Lazaric (Facebook AI Research) · Lerrel Pinto (NYU/Berkeley)

22. Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo

James Brofos (Yale University) · Roy Lederman (Yale University)

23. Deep Reinforcement Learning amidst Continual Structured Non-Stationarity

Annie Xie (Stanford University) · James Harrison (Stanford University) · Chelsea Finn (Stanford)

24. Off-Policy Confidence Sequences

Nikos Karampatziakis (Microsoft) · Paul Mineiro (Microsoft) · Aaditya Ramdas (Carnegie Mellon University)

25. Deeply-Debiased Off-Policy Interval Estimation

Chengchun Shi (London School of Economics and Political Science) · Runzhe Wan (North Carolina State University) · Victor Chernozhukov (MIT) · Rui Song (North Carolina State University)

26. Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

Yangjun Ruan (University of Toronto) · Karen Ullrich (FAIR) · Daniel Severo (University of Toronto) · James Townsend () · Ashish Khisti (Univ. of Toronto) · Arnaud Doucet (Oxford University) · Alireza Makhzani (University of Toronto) · Chris Maddison (University of Toronto)

27. Logarithmic Regret for Reinforcement Learning with Linear Function Approximation

Jiafan He (University of California, Los Angeles) · Dongruo Zhou (UCLA) · Quanquan Gu (University of California, Los Angeles)

28. Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

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)

29. Monotonic Robust Policy Optimization with Model Discrepancy

yuankun jiang (Shanghai Jiao Tong University) · Chenglin Li (Shanghai Jiao Tong University) · Wenrui Dai (Shanghai Jiao Tong University) · Junni Zou (Shanghai Jiao Tong University) · Hongkai Xiong (Shanghai Jiao Tong University)

30. Guided Exploration with Proximal Policy Optimization using a Single Demonstration

Gabriele Libardi (Pompeu Fabra University) · Gianni De Fabritiis (Universitat Pompeu Fabra) · Sebastian Dittert (Universitat Pompeu Fabra)

31. Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration

Seungyul Han (KAIST) · Youngchul Sung (KAIST)

32. On-Policy Reinforcement Learning for the Average-Reward Criterion

Yiming Zhang (New York University) · Keith Ross (New York University Shanghai)

33. UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

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)

34. Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation

Christopher Dance (NAVER LABS Europe) · Perez Julien (Naver Labs Europe) · Théo Cachet (Naver Labs Europe)

35. Feature Clustering for Support Identification in Extreme Regions

Hamid Jalalzai (Inria) · Rémi Leluc (Télécom Paris)

36. Multi-Task Reinforcement Learning with Context-based Representations

Shagun Sodhani (Facebook AI Research) · Amy Zhang (FAIR / McGill) · Joelle Pineau (McGill, Facebook)

37. Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret

Asaf Cassel (Tel Aviv University) · Tomer Koren (Tel Aviv University and Google)

38. Learning and Planning in Average-Reward Markov Decision Processes

Yi Wan (University of Alberta) · Abhishek Naik (University of Alberta) · Richard Sutton (DeepMind / Univ Alberta)

39. MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration

Jin Zhang (Tsinghua University) · Jianhao Wang (Tsinghua University) · Hao Hu (Tsinghua University) · Tong Chen (Tsinghua University) · Yingfeng Chen (NetEase Fuxi AI Lab) · Changjie Fan (NetEase Fuxi AI Lab) · Chongjie Zhang (Tsinghua University)

40. A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning

Abi Komanduru (Purdue University) · Jean Honorio (Purdue University)

41. Safe Reinforcement Learning with Linear Function Approximation

Sanae Amani (University of California, Los Angeles) · Christos Thrampoulidis (University of British Columbia) · Lin Yang (UCLA)

42. Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning

Sebastian Curi (ETH) · Ilija Bogunovic (ETH Zurich) · Andreas Krause (ETH Zurich)

43. A Precise Performance Analysis of Support Vector Regression

Houssem Sifaou (King Abdullah University of Science and Technology (KAUST)) · Abla Kammoun (KAUST) · Mohamed-Slim Alouini (King Abdullah University of Science and Technology )

44. Generalizable Episodic Memory for Deep Reinforcement Learning

Hao Hu (Tsinghua University) · Jianing Ye (Peking University) · Guangxiang Zhu (Tsinghua University) · Zhizhou Ren (University of Illinois at Urbana-Champaign) · Chongjie Zhang (Tsinghua University)

45. Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping

Dongruo Zhou (UCLA) · Jiafan He (University of California, Los Angeles) · Quanquan Gu (University of California, Los Angeles)

46. Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games

Hongyi Guo (Northwestern University) · Zuyue Fu (Northwestern) · Zhuoran Yang (Princeton) · Zhaoran Wang (Northwestern U)

47. Adaptive Sampling for Best Policy Identification in Markov Decision Processes

Aymen Al Marjani (ENS Lyon) · Alexandre Proutiere (KTH Royal Institute of Technology)

48. Inverse Constrained Reinforcement Learning

Shehryar Malik (Information Technology University) · Usman Anwar (Information Technlogy University, Lahore.) · Alireza Aghasi (Georgia State University) · Ali Ahmed (Information Technology University)

49. Self-Paced Context Evaluation for Contextual Reinforcement Learning

Theresa Eimer (Leibniz Universität Hannover) · André Biedenkapp (University of Freiburg) · Frank Hutter (University of Freiburg and Bosch Center for Artificial Intelligence) · Marius Lindauer (Leibniz University Hannover)

50. On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients

Difan Zou (UCLA) · Quanquan Gu (University of California, Los Angeles)

51. DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs

Vincent Plassier (Huawei) · Maxime Vono (Lagrange Mathematics and Computing Research Center) · Alain Durmus (ENS Paris Saclay) · Eric Moulines (Ecole Polytechnique)

52. Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation

Qian Zhang (Purdue University) · Yilin Zheng (Purdue university) · Jean Honorio (Purdue University)

53. Optimal Thompson Sampling strategies for support-aware CVaR bandits

Dorian Baudry (CNRS/INRIA) · Romain Gautron (CIRAD - CGIAR) · Emilie Kaufmann (CNRS, Univ. Lille) · Odalric-Ambrym Maillard (Inria Lille - Nord Europe)

54. High Confidence Generalization for Reinforcement Learning

James Kostas (University of Massachusetts Amherst) · Yash Chandak (University of Massachusetts Amherst) · Scott M Jordan (University of Massachusetts) · Georgios Theocharous (Adobe Research) · Philip Thomas (University of Massachusetts Amherst)

55. Robust Asymmetric Learning in POMDPs

Andrew Warrington (University of Oxford) · Jonathan Lavington (University of British Columbia) · Adam Scibior (University of British Columbia) · Mark Schmidt (University of British Columbia) · Frank Wood (University of British Columbia)

56. Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach

Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton University) · Zhaoran Wang (Northwestern U)

57. Decoupling Value and Policy for Generalization in Reinforcement Learning

Roberta Raileanu (NYU) · Rob Fergus (Facebook / NYU)

58. Learning Routines for Effective Off-Policy Reinforcement Learning

Edoardo Cetin (King's College London) · Oya Celiktutan (King's College London)

59. Emergent Social Learning via Multi-agent Reinforcement Learning

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

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

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

61. Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks

Sungryull Sohn (University of Michigan) · Sungtae Lee (Yonsei University) · Jongwook Choi (University of Michigan) · Harm van Seijen (Microsoft Research) · Mehdi Fatemi (Microsoft Research) · Honglak Lee (Google / U. Michigan)

62. What Structural Conditions Permit Generalization in Reinforcement Learning?

Simon Du (University of Washington) · Sham Kakade (University of Washington) · Jason Lee (Princeton) · Shachar Lovett (University of California San Diego) · Gaurav Mahajan (UCSD) · Wen Sun (Cornell University) · Ruosong Wang (Carnegie Mellon University)

63. On Proximal Policy Optimization's Heavy-tailed Gradients

Saurabh Garg (Carnegie Mellon University) · Joshua Zhanson (Carnegie Mellon University) · Emilio Parisotto (Carnegie Mellon University) · Adarsh Prasad (Carnegie Mellon University) · Zico Kolter (Carnegie Mellon University / Bosch Center for AI) · Sivaraman Balakrishnan (CMU) · Zachary Lipton (Carnegie Mellon University) · Ruslan Salakhutdinov (Carnegie Mellen University) · Pradeep Ravikumar (Carnegie Mellon University)

64. The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks

Xiaocheng Li (Imperial College London) · Chunlin Sun (Stanford University) · Yinyu Ye (Standord)

65. Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity

Dhruv Malik (Carnegie Mellon University) · Aldo Pacchiano (UC Berkeley) · Vishwak Srinivasan (Carnegie Mellon University) · Yuanzhi Li (CMU)

66. Improved Regret Bound and Experience Replay in Regularized Policy Iteration

Nevena Lazic (DeepMind) · Dong Yin (DeepMind) · Yasin Abbasi-Yadkori (Adobe Research) · Csaba Szepesvari (DeepMind/University of Alberta)

67. Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning

Gen Li (Tsinghua University, China) · Changxiao Cai (Princeton University) · Yuxin Chen (Princeton University) · Yuantao Gu (Tsinghua University) · Yuting Wei (Carnegie Mellon University) · Yuejie Chi (CMU)

68. The Emergence of Individuality in Multi-Agent Reinforcement Learning

Jiechuan Jiang (Peking University) · Zongqing Lu (Peking University)

69. MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning

Kevin Li (UC Berkeley) · Abhishek Gupta (UC Berkeley) · Ashwin D Reddy (UC Berkeley) · Vitchyr Pong (UC Berkeley) · Aurick Zhou (UC Berkeley) · Justin Yu (Berkeley) · Sergey Levine (UC Berkeley)

70. Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

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

71. Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time

Weichen Wang (Two Sigma Investments, LP) · Jiequn Han (Princeton University) · Zhuoran Yang (Princeton University) · Zhaoran Wang (Northwestern)

72. Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing

Kaixin Wang (National University of Singapore) · Kuangqi Zhou (National University of Singapore) · Qixin Zhang (city university of hong kong) · Jie Shao (Fudan University) · Bryan Hooi (National University of Singapore) · Jiashi Feng (National University of Singapore)

73. Model-Free and Model-Based Policy Evaluation when Causality is Uncertain

David Bruns-Smith (UC Berkeley)

74. GMAC: A Distributional Perspective on Actor-Critic Framework

Daniel Nam (KC Machine Learning Lab) · Younghoon Kim (KC-ML2) · Chan Youn Park (KC ML2)

75. Offline Reinforcement Learning with Fisher Divergence Critic Regularization

Ilya Kostrikov (Google/New York University) · Rob Fergus (DeepMind) · Jonathan Tompson (Google Brain) · Ofir Nachum (Google Brain)

76. A Sharp Analysis of Model-based Reinforcement Learning with Self-Play

Qinghua Liu (Princeton University) · Tiancheng Yu (MIT) · Yu Bai (Salesforce Research) · Chi Jin (Princeton University)

77. Density Constrained Reinforcement Learning

Zengyi Qin (MIT) · Yuxiao Chen (California Institute of Technology) · Chuchu Fan (MIT)

78. Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

Evan Liu (Stanford University) · Aditi Raghunathan (Stanford) · Percy Liang (Stanford University) · Chelsea Finn (Stanford)

79. CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee

Tengyu Xu (The Ohio State University) · Yingbin LIANG (The Ohio State University) · Guanghui Lan (Georgia Institute of Technology)

80. Large-Scale Multi-Agent Deep FBSDEs

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

81. Model-based Reinforcement Learning for Continuous Control with Posterior Sampling

Ying Fan (University of Wisconsin-Madison) · Yifei Ming (University of Wisconsin-Madison)

82. SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning

Lokesh Chandra Das (The University of Memphis) · Myounggyu Won (University of Memphis)

83. Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees

Kishan Panaganti (TAMU) · Dileep Kalathil (TAMU)

84. Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Michael Chang (UC Berkeley) · Sid Kaushik (UCB) · Sergey Levine (UC Berkeley) · Thomas Griffiths (Princeton University)

85. Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality

Tengyu Xu (The Ohio State University) · Zhuoran Yang (Princeton University) · Zhaoran Wang (Northwestern U) · Yingbin LIANG (The Ohio State University)

86. Matrix Completion with Model-free Weighting

Jiayi Wang (Texas A&M University) · Raymond K. W. Wong (Texas A&M University) · Xiaojun Mao (Fudan University) · Kwun Chuen Gary Chan (University of Washington)

87. Improved Corruption Robust Algorithms for Episodic Reinforcement Learning

Yifang Chen (University of Washington) · Simon Du (University of Washington) · Kevin Jamieson (University of Washington)

88. Offline Meta-Reinforcement Learning with Advantage Weighting

Eric Mitchell (Stanford) · Rafael Rafailov (Stanford University) · Xue Bin Peng (UC Berkeley) · Sergey Levine (University of California, Berkeley) · Chelsea Finn (Stanford)

89. Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning

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)

90. Automatic RNN Repair via Model-based Analysis

Xiaofei Xie (Nanyang Technological University) · Wenbo Guo (Pennsylvania State University) · Lei Ma (University of Alberta) · Wei Le (Iowa State University ) · Jian Wang (Nanyang Technological University) · Lingjun Zhou (College of Intelligence and Computing,Tianjin University) · Yang Liu (Nanyang Technology University, Singapore) · Xinyu Xing (The Pennsylvania State University)

91. PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Kimin Lee (UC Berkeley) · Laura Smith (UC Berkeley) · Pieter Abbeel (UC Berkeley & Covariant)

92. Recomposing the Reinforcement Learning Building Blocks with Hypernetworks

shai keynan (Bar Ilan University) · Elad Sarafian (Bar-Ilan University) · Sarit Kraus (Bar-Ilan University)

93. PODS: Policy Optimization via Differentiable Simulation

Miguel Angel Zamora Mora (ETH Zurich) · Momchil Peychev (ETH Zurich) · Sehoon Ha (Georgia Institute of Technology) · Martin Vechev (ETH Zurich) · Stelian Coros (ETH Zurich)

94. Bootstrapping Fitted Q-Evaluation for Off-Policy Inference

Botao Hao (Princeton University) · Xiang Ji (Princeton University) · Yaqi Duan (Princeton University) · Hao Lu (Princeton University) · Csaba Szepesvari (DeepMind/University of Alberta) · Mengdi Wang (Princeton University)

95. Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient

Botao Hao (Princeton University) · Yaqi Duan (Princeton University) · Tor Lattimore (DeepMind) · Csaba Szepesvari (DeepMind/University of Alberta) · Mengdi Wang (Princeton University)

96. Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning

Henry Charlesworth (University of Warwick) · Giovanni Montana (University of Warwick)

97. Monte Carlo Variational Auto-Encoders

Achille Thin (Ecole polytechnique) · Nikita Kotelevskii (Skolkovo Institute of Science and Technology) · Arnaud Doucet (Oxford University) · Alain Durmus (ENS Paris Saclay) · Eric Moulines (Ecole Polytechnique) · Maxim Panov (Skolkovo Institute of Science and Technology)

98. Fast active learning for pure exploration in reinforcement learning

Pierre MENARD (Inria) · Omar Darwiche Domingues (Inria) · Anders Jonsson (Universitat Pompeu Fabra) · Emilie Kaufmann (CNRS, Univ. Lille) · Edouard Leurent () · Michal Valko (DeepMind / Inria / ENS Paris-Saclay)

99. UCB Momentum Q-learning: Correcting the bias without forgetting

Pierre MENARD (Inria) · Omar Darwiche Domingues (Inria) · Xuedong Shang (Inria) · Michal Valko (DeepMind / Inria / ENS Paris-Saclay)

100. Continuous-time Model-based Reinforcement Learning

Cagatay Yildiz (Aalto University) · Markus Heinonen (Aalto University) · Harri Lähdesmäki (Aalto University)

101. Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

Hiroki Furuta (The University of Tokyo) · Tatsuya Matsushima (The University of Tokyo) · Tadashi Kozuno (University of Alberta) · Yutaka Matsuo (University of Tokyo) · Sergey Levine (UC Berkeley) · Ofir Nachum (Google Brain) · Shixiang Gu (Google)

102. Kernel-Based Reinforcement Learning: Finite-Time Analysis for a Practical Algorithm

Omar Darwiche Domingues (Inria) · Pierre Menard (Inria) · Matteo Pirotta (Facebook AI Research) · Emilie Kaufmann (CNRS, Univ. Lille) · Michal Valko (DeepMind / Inria / ENS Paris-Saclay)

103. Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

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

104. A Novel Method to Solve Neural Knapsack Problems

Duanshun Li (University of Alberta) · Jing Liu (Walmart Research Lab.) · Dongeun Lee (Texas A&M University-Commerce) · Ali Seyedmazloom (George Mason Univeristy) · Giridhar Kaushik (George Mason Univeristy) · Kookjin Lee (Sandia National Laboratories) · Noseong Park (Yonsei University, Korea)

105. Data-efficient Hindsight Off-policy Option Learning

Markus Wulfmeier (DeepMind) · Dushyant Rao (DeepMind) · Roland Hafner (DeepMind) · Thomas Lampe (DeepMind) · Abbas Abdolmaleki (DeepMind) · Tim Hertweck (DeepMind) · Michael Neunert (Google DeepMind) · Dhruva Tirumala Bukkapatnam (DeepMind) · Noah Siegel (DeepMind) · Nicolas Heess (DeepMind) · Martin Riedmiller (DeepMind)

106. A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization

Andrew Campbell (University of Oxford) · Wenlong Chen (University of Cambridge) · Vincent Stimper (University of Cambridge) · Jose Miguel Hernandez-Lobato (University of Cambridge) · Yichuan Zhang (Boltzbit Limited)

107. Muesli: Combining Improvements in Policy Optimization

Matteo Hessel (Deep Mind) · Ivo Danihelka (DeepMind) · Fabio Viola (DeepMind) · Arthur Guez (Google DeepMind) · Simon Schmitt (DeepMind) · Laurent Sifre (DeepMind) · Theophane Weber (DeepMind) · David Silver (Google DeepMind) · Hado van Hasselt (DeepMind)

108. Annealed Flow Transport Monte Carlo

Michael Arbel (University College London) · Alexander Matthews (DeepMind) · Arnaud Doucet (Google DeepMind)

109. Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning

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)

110. Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity

Georgios Amanatidis (University of Essex) · Federico Fusco (Sapienza University of Rome) · Philip Lazos (Sapienza University of Rome) · Stefano Leonardi (Sapienza University of Rome) · Alberto Marchetti-Spaccamela (Sapienza University of Rome) · Rebecca Reiffenhäuser (Sapienza University of Rome)

111. Reinforcement Learning for Cost-Aware Markov Decision Processes

Wesley Suttle (Stony Brook University) · Kaiqing Zhang (University of Illinois at Urbana-Champaign/MIT) · Zhuoran Yang (Princeton University) · Ji Liu (Stony Brook University) · David N Kraemer (Stony Brook University)

112. Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision

Johan Björck (Cornell) · Xiangyu Chen (Cornell University) · Christopher De Sa (Cornell) · Carla Gomes (Cornell University) · Kilian Weinberger (Cornell University)

113. Reward Identification in Inverse Reinforcement Learning

Kuno Kim (Stanford University) · Shivam Garg (Stanford University) · Kirankumar Shiragur (Stanford University) · Stefano Ermon (Stanford University)

114. Offline Reinforcement Learning with Pseudometric Learning

Robert Dadashi (Google AI Residency Program) · Shideh Rezaeifar (University of Geneva) · Nino Vieillard (Google Brain) · Léonard Hussenot (Google Research, Brain Team) · Olivier Pietquin (GOOGLE BRAIN) · Matthieu Geist (Google)

115. Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning

Jongwook Choi (University of Michigan) · Archit Sharma () · Honglak Lee (Google / U. Michigan) · Sergey Levine (Google) · Shixiang Gu (Google)

116. Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Luisa Zintgraf (University of Oxford) · Leo Feng (Mila) · Cong Lu (University of Oxford) · Maximilian Igl (University of Oxford) · Kristian Hartikainen (UC Berkeley) · Katja Hofmann (Microsoft) · Shimon Whiteson (University of Oxford)

117. PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

Angelos Filos (University of Oxford) · Clare Lyle (University of Oxford) · Yarin Gal (University of Oxford) · Sergey Levine (UC Berkeley) · Natasha Jaques (Google Brain, UC Berkeley) · Gregory Farquhar (University of Oxford)

118. Safe Reinforcement Learning Using Advantage-Based Intervention

Nolan Wagener (Georgia Tech) · Ching-An Cheng (Microsoft Research) · Byron Boots (University of Washington)

119. Decoupling Representation Learning from Reinforcement Learning

Adam Stooke (UC Berkeley) · Kimin Lee (UC Berkeley) · Pieter Abbeel (UC Berkeley & Covariant) · Michael Laskin (UC Berkeley)

120. Goal-Conditioned Reinforcement Learning with Imagined Subgoals

Elliot Chane-Sane (INRIA Paris) · Cordelia Schmid (Inria/Google) · Ivan Laptev (INRIA Paris)

121. A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

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)

122. Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

Ole-Christoffer Granmo (University of Agder) · Rohan Kumar Yadav (University of Agder) · Kuruge Darshana Abeyrathna (University of Agder, Norway) · Lei Jiao (University of Agder) · Rupsa Saha (University of Agder) · Bimal Bhattarai (University of Agder) · Saeed Rahimi Gorji (University of Agder) · Morten Goodwin (University of Agder)

123. RRL: Resnet as representation for Reinforcement Learning

Rutav M Shah (Indian Institute of Technology, Kharagpur) · Vikash Kumar (Univ. Of Washington)

124. Model-Based Reinforcement Learning via Latent-Space Collocation

Oleh Rybkin (University of Pennsylvania) · Chuning Zhu (University of Pennsylvania) · Anusha Nagabandi (UC Berkeley) · Kostas Daniilidis (University of Pennsylvania) · Igor Mordatch (Google Brain) · Sergey Levine (UC Berkeley)

125. Robust Policy Gradient against Strong Data Corruption

Xuezhou Zhang (UW-Madison) · Yiding Chen (University of Wisconsin-Madison) · Jerry Zhu (University of Wisconsin-Madison) · Wen Sun (Cornell University)

126. OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

Jongmin Lee (KAIST) · Wonseok Jeon (MILA, McGill University) · Byung-Jun Lee (KAIST) · Joelle Pineau (McGill University / Facebook) · Kee-Eung Kim (KAIST)

127. Quantum algorithms for reinforcement learning with a generative model

Ashish Kapoor (Microsoft Research) · Robin Kothari (Microsoft) · Martin Roetteler (Microsoft) · Aarthi Sundaram (Microsoft) · Daochen Wang (University of Maryland)

128. Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations

Angeliki Kamoutsi (ETH Zurich) · Goran Banjac (ETH Zurich) · John Lygeros (ETH Zürich)

129. Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

Eli Meirom (NVIDIA Research) · Haggai Maron (NVIDIA Research) · Shie Mannor (Technion) · Gal Chechik (NVIDIA / Bar-Ilan University)

130. Emphatic Algorithms for Deep Reinforcement Learning

Tian Jiang (Facebook) · Tom Zahavy (DeepMind) · Zhongwen Xu (DeepMind) · Adam White (Deepmind, University of Alberta) · Matteo Hessel (Deep Mind) · Charles Blundell (DeepMind) · Hado van Hasselt (DeepMind)

131. Learning node representations using stationary flow prediction on large payment and cash transaction networks

Ciwan Ceylan (KTH Royal Institute of Technology & SEB) · Salla Franzén (SEB AB) · Florian T. Pokorny (KTH Royal Institute of Technology)

132. Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

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)

133. Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

Austin W. Hanjie (Princeton University) · Victor Zhong (University of Washington) · Karthik Narasimhan (Princeton)

134. Discovering symbolic policies with deep reinforcement learning

Sookyung Kim (Lawrence Livermore National Laboratory) · Mikel Landajuela (Lawrence Livermore National Laboroatory) · Brenden Petersen (Lawrence Livermore National Laboratory) · Claudio Santiago (LLNL) · Ruben Glatt (LLNL) · Nathan Mundhenk (Lawrence Livermore National Labs) · Jacob Pettit (Lawrence Livermore National Laboratory) · Daniel Faissol (Lawrence Livermore National Laboratory)

135. A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation

Scott Fujimoto (McGill University) · David Meger (McGill University) · Doina Precup (McGill University / DeepMind)

136. Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

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

137. PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration

Yuda Song (University of California, San Diego) · Wen Sun (Cornell University)

138. Characterizing the Gap Between Actor-Critic and Policy Gradient

Junfeng Wen (University of Alberta) · Saurabh Kumar (Stanford) · Ramki Gummadi (Google Brain) · Dale Schuurmans (University of Alberta)

139. Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition

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)

140. Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective

Florin Gogianu (Bitdefender) · Tudor Berariu (Imperial College London) · Mihaela Rosca (DeepMind) · Claudia Clopath (Imperial College London) · Lucian Busoniu (Technical University of Cluj-Napoca) · Razvan Pascanu (DeepMind)

141. Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills

Yevgen Chebotar (Google) · Karol Hausman (Google Brain) · Yao Lu (Google Research) · Ted Xiao (Google) · Dmitry Kalashnikov (Google Inc.) · Jacob Varley (Google) · Alexander Irpan (Google) · Benjamin Eysenbach (CMU, Google Brain) · Ryan Julian (Google) · Chelsea Finn (Google Brain) · Sergey Levine (Google)

142. When is Pessimism Warranted in Batch Policy Optimization?

Chenjun Xiao (Google / University of Alberta) · Yifan Wu (Carnegie Mellon University) · Jincheng Mei (University of Alberta / Google Brain) · Bo Dai (Google Brain) · Tor Lattimore (DeepMind) · Lihong Li (Google Research) · Csaba Szepesvari (DeepMind/University of Alberta) · Dale Schuurmans (Google / University of Alberta)

143. Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization

Wesley Chung (Mila / McGill University) · Valentin Thomas (MILA) · Marlos C. Machado (Google Brain) · Nicolas Le Roux (Google)

144. Towards Tight Bounds on the Sample Complexity of Average-reward MDPs

Yujia Jin (Stanford University) · Aaron Sidford (Stanford)

145. Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods

Chris Nota (University of Massachusetts Amherst) · Philip Thomas (University of Massachusetts Amherst) · Bruno C. da Silva (University of Massachusetts)

146. Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Thomas Mesnard (DeepMind) · Theophane Weber (DeepMind) · Fabio Viola (DeepMind) · Shantanu Thakoor (DeepMind) · Alaa Saade (DeepMind) · Anna Harutyunyan (DeepMind) · Will Dabney (DeepMind) · Thomas Stepleton (DeepMind) · Nicolas Heess (DeepMind) · Arthur Guez (Google DeepMind) · Eric Moulines (Ecole Polytechnique) · Marcus Hutter (DeepMind) · Lars Buesing (Deepmind) · Remi Munos (DeepMind)

147. Randomized Exploration in Reinforcement Learning with General Value Function Approximation

Haque Ishfaq (MILA / McGill University) · Qiwen Cui (Peking University) · Alex Ayoub (University of Alberta) · Viet Nguyen (McGill, Mila) · Zhuoran Yang (Princeton University) · Zhaoran Wang (Northwestern U) · Doina Precup (McGill University / DeepMind) · Lin Yang (UCLA)

148. Structured World Belief for Reinforcement Learning in POMDP

Gautam Singh (Rutgers University) · Skand Peri (Rutgers University, New Jersey) · Junghyun Kim (Rutgers University) · Hyunseok Kim (Electronics and Telecommunications Research Institute (ETRI), Korea) · Sungjin Ahn (Rutgers University)

149. EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL

Seyed Kamyar Seyed Ghasemipour (University of Toronto) · Dale Schuurmans (Google / University of Alberta) · Shixiang Gu (Google)

150. Policy Gradient Bayesian Robust Optimization for Imitation Learning

Daniel Brown (University of Texas at Austin) · Ashwin Balakrishna (University of California, Berkeley) · Zaynah Javed (UC Berkeley) · Satvik Sharma (UC Berkeley) · Jerry Zhu (UC Berkeley) · Marek Petrik (University of New Hampshire) · Anca Dragan (University of California, Berkeley) · Ken Goldberg (UC Berkeley)

151. Reinforcement Learning of Implicit and Explicit Control Flow Instructions

Ethan Brooks (University of Michigan) · Janarthanan Rajendran (University of Michigan) · Richard Lewis (University of Michigan) · Satinder Singh (University of Michigan)

152. SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

Xiangjun Wang (inspir.ai) · Junxiao SONG (inspir.ai) · Penghui Qi (InspirAI) · Peng Peng (inspir.ai) · Zhenkun Tang (inspir.ai) · Wei Zhang (inspir.ai) · Weimin Li (inspir.ai) · Xiongjun Pi (inspir.ai) · Jujie He (inspir.ai) · Chao Gao (inspir.ai) · Haitao Long (inspir.ai) · Quan Yuan (inspir.ai)

153. Reinforcement Learning Under Moral Uncertainty

Adrien Ecoffet (OpenAI) · Joel Lehman ()

154. Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards

Susan Amin (McGill University) · Maziar Gomrokchi (McGill University) · Hossein Aboutalebi (University of Waterloo) · Harsh Satija (McGill University) · Doina Precup (McGill University / DeepMind)

155. Conservative Objective Models for Effective Offline Model-Based Optimization

Brandon L Trabucco (UC Berkeley) · Aviral Kumar (UC Berkeley) · Xinyang Geng (UC Berkeley) · Sergey Levine (UC Berkeley)

156. State Relevance for Off-Policy Evaluation

Simon Shen (Harvard University) · Yecheng Ma (University of Pennsylvania) · Omer Gottesman (Harvard University) · Finale Doshi-Velez (Harvard University)

157. Failure Modes and Opportunities in Out-of-distribution Detection with Deep Generative Models

Lily Zhang (New York University) · Mark Goldstein (New York University) · Rajesh Ranganath (New York University)

158. Temporal Predictive Coding For Model-Based Planning In Latent Space

Tung Nguyen (VinAI Research, Vietnam) · Rui Shu (Stanford University) · Tuan Pham (VinAI Research) · Hung Bui (VinAI Research) · Stefano Ermon (Stanford University)

159. Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

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

160. FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning

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)

161. Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions

Shuang Qiu (University of Michigan) · Zhuoran Yang (Princeton University) · Xiaohan Wei (Facebook) · Jieping Ye (University of Michigan) · Zhaoran Wang (Northwestern U)

162. On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

Shuang Qiu (University of Michigan) · Zhuoran Yang (Princeton University) · Jieping Ye (University of Michigan) · Zhaoran Wang (Northwestern U)

163. Nonparametric Hamiltonian Monte Carlo

Carol Mak (University of Oxford) · Fabian Zaiser (University of Oxford) · Luke Ong (University of Oxford)

补充篇

164. Principled Exploration via Optimistic Bootstrapping and Backward Induction

https://arxiv.org/abs/2105.06022