1. Relabeling Experience with Inverse RL: Hindsight Inference for Policy Improvement

Ben Eysenbach (Carnegie Mellon University) · XINYANG GENG (UC Berkeley) · Sergey Levine (UC Berkeley) · Russ Salakhutdinov (Carnegie Mellon University)

2. Generalised Bayesian Filtering via Sequential Monte Carlo

Ayman Boustati (University of Warwick) · Omer Deniz Akyildiz (University of Warwick) · Theodoros Damoulas (University of Warwick & The Alan Turing Institute) · Adam Johansen (University of Warwick)

3. Softmax Deep Double Deterministic Policy Gradients

Ling Pan (Tsinghua University) · Qingpeng Cai (Alibaba Group) · Longbo Huang (IIIS, Tsinghua Univeristy)

4. Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

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

5. Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks

Jing Xu (Peking University) · Fangwei Zhong (Peking University) · Yizhou Wang (Peking University)

6. Off-Policy Imitation Learning from Observations

Zhuangdi Zhu (Michigan State University) · Kaixiang Lin (Michigan State University) · Bo Dai (Google Brain) · Jiayu Zhou (Michigan State University)

7. Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

Vitaly Kurin (University of Oxford) · Saad Godil (NVIDIA) · Shimon Whiteson (University of Oxford) · Bryan Catanzaro (NVIDIA)

8. DISK: Learning local features with policy gradient

MichaÅ‚ Tyszkiewicz (EPFL) · Pascal Fua (EPFL, Switzerland) · Eduard Trulls (Google)

9. Learning Individually Inferred Communication for Multi-Agent Cooperation

Ziluo Ding (Peking University) · Tiejun Huang (Peking University) · Zongqing Lu (Peking University)

10. Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting

Jorge Mendez (University of Pennsylvania) · Boyu Wang (University of Western Ontario) · Eric Eaton (University of Pennsylvania)

11. Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm

Tianyi Lin (UC Berkeley) · Nhat Ho (University of Texas at Austin) · Xi Chen (New York University) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)

12. Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards

Yijie Guo (University of Michigan) · Jongwook Choi (University of Michigan) · Marcin Moczulski (Google Brain) · Shengyu Feng (University of Illinois Urbana Champaign) · Samy Bengio (Google Research, Brain Team) · Mohammad Norouzi (Google Brain) · Honglak Lee (Google / U. Michigan)

13. Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition

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

14. Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping

Yujing Hu (NetEase Fuxi AI Lab) · Weixun Wang (Tianjin University) · Hangtian Jia (Netease Fuxi AI Lab) · Yixiang Wang (University of Science and Technology of China) · Yingfeng Chen (NetEase Fuxi AI Lab) · Jianye Hao (Tianjin University) · Feng Wu (University of Science and Technology of China) · Changjie Fan (NetEase Fuxi AI Lab)

15. Effective Diversity in Population Based Reinforcement Learning

Jack Parker-Holder (University of Oxford) · Aldo Pacchiano (UC Berkeley) · Krzysztof M Choromanski (Google Brain Robotics) · Stephen J Roberts (University of Oxford)

16. A Boolean Task Algebra for Reinforcement Learning

Geraud Nangue Tasse (University of the Witwatersrand) · Steven James (University of the Witwatersrand) · Benjamin Rosman (University of the Witwatersrand / CSIR)

17. A new convergent variant of Q-learning with linear function approximation

Diogo Carvalho (GAIPS, INESC-ID) · Francisco S. Melo (IST/INESC-ID) · Pedro A. Santos (Instituto Superior TÃ©cnico)

18. Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

Zhiyuan Xu (Syracuse University) · Kun Wu (Syracuse University) · Zhengping Che (DiDi AI Labs, Didi Chuxing) · Jian Tang (DiDi AI Labs, DiDi Chuxing) · Jieping Ye (Didi Chuxing)

19. Multi-task Batch Reinforcement Learning with Metric Learning

Jiachen Li (University of California, San Diego) · Quan Vuong (University of California San Diego) · Shuang Liu (University of California, San Diego) · Minghua Liu (UCSD) · Kamil Ciosek (Microsoft) · Henrik Christensen (UC San Diego) · Hao Su (UCSD)

20. Demystifying Orthogonal Monte Carlo and Beyond

Han Lin (Columbia University) · Haoxian Chen (Columbia University) · Krzysztof M Choromanski (Google Brain Robotics) · Tianyi Zhang (Columbia University) · Clement Laroche (Columbia University)

21. On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems

Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Bin Hu (University of Illinois at Urbana-Champaign) · Tamer Basar (University of Illinois at Urbana-Champaign)

22. Towards Playing Full MOBA Games with Deep Reinforcement Learning

Deheng Ye (Tencent) · Guibin Chen (Tencent) · Wen Zhang (Tencent) · chen sheng (qq) · Bo Yuan (Tencent) · Bo Liu (Tencent) · Jia Chen (Tencent) · Hongsheng Yu (Tencent) · Zhao Liu (Tencent) · Fuhao Qiu (Tencent AI Lab) · Liang Wang (Tencent) · Tengfei Shi (Tencent) · Yinyuting Yin (Tencent) · Bei Shi (Tencent AI Lab) · Lanxiao Huang (Tencent) · qiang fu (Tencent AI Lab) · Wei Yang (Tencent AI Lab) · Wei Liu (Tencent AI Lab)

23. How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization

Pierluca D'Oro (MILA) · Wojciech JaÅ›kowski (NNAISENSE SA)

24. Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting

Ziping Xu (University of Michigan) · Ambuj Tewari (University of Michigan)

25. HiPPO: Recurrent Memory with Optimal Polynomial Projections

Albert Gu (Stanford) · Tri Dao (Stanford University) · Stefano Ermon (Stanford) · Atri Rudra (University at Buffalo, SUNY) · Christopher RÃ© (Stanford)

26. Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

Julien Roy (Mila) · Paul Barde (Quebec AI institute - Ubisoft La Forge) · FÃ©lix G Harvey (Polytechnique MontrÃ©al) · Derek Nowrouzezahrai (McGill University) · Chris Pal (MILA, Polytechnique MontrÃ©al, Element AI)

27. Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs

Chung-Wei Lee (University of Southern California) · Haipeng Luo (University of Southern California) · Chen-Yu Wei (University of Southern California) · Mengxiao Zhang (University of Southern California)

28. Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization

Nan Jiang (University of Illinois at Urbana-Champaign) · Jiawei Huang (University of Illinois at Urbana-Champaign)

29. Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning

Nathan Kallus (Cornell University) · Angela Zhou (Cornell University)

30. Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition

Tiancheng Jin (University of Southern California) · Haipeng Luo (University of Southern California)

31. Learning Retrospective Knowledge with Reverse Reinforcement Learning

Shangtong Zhang (University of Oxford) · Vivek Veeriah (University of Michigan) · Shimon Whiteson (University of Oxford)

32. Combining Deep Reinforcement Learning and Search for Imperfect-Information Games

Noam Brown (Facebook AI Research) · Anton Bakhtin (Facebook AI Research) · Adam Lerer (Facebook AI Research) · Qucheng Gong (Facebook AI Research)

33. Variance reduction for Langevin Monte Carlo in high dimensional sampling problems

ZHIYAN DING (University of Wisconsin-Madison) · Qin Li (University of Wisconsin-Madison)

34. POMO: Policy Optimization with Multiple Optima for Reinforcement Learning

Yeong-Dae Kwon (Samsung SDS) · Jinho Choo (Samsung SDS) · Byoungjip Kim (Samsung SDS) · Iljoo Yoon (Samsung SDS) · Youngjune Gwon (Samsung SDS) · Seungjai Min (Samsung SDS)

35. Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables

Guangyao Zhou (Vicarious AI)

36. Self-Paced Deep Reinforcement Learning

Pascal Klink (TU Darmstadt) · Carlo D'Eramo (TU Darmstadt) · Jan Peters (TU Darmstadt & MPI Intelligent Systems) · Joni Pajarinen (TU Darmstadt)

37. Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

Sebastian Curi (ETH ZÃ¼rich) · Felix Berkenkamp (Bosch Center for Artificial Intelligence) · Andreas Krause (ETH Zurich)

38. Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies

Nathan Kallus (Cornell University) · Masatoshi Uehara (Cornell University)

39. Off-Policy Evaluation and Learning for External Validity under a Covariate Shift

Masatoshi Uehara (Cornell University) · Masahiro Kato (The University of Tokyo) · Shota Yasui (Cyberagent)

40. Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms

Tengyu Xu (The Ohio State University) · Zhe Wang (Ohio State University) · Yingbin Liang (The Ohio State University)

41. Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine

Jiajin Li (The Chinese University of Hong Kong) · Caihua Chen (Nanjing University) · Anthony Man-Cho So (CUHK)

42. A maximum-entropy approach to off-policy evaluation in average-reward MDPs

Nevena Lazic (DeepMind) · Dong Yin (DeepMind) · Mehrdad Farajtabar (DeepMind) · Nir Levine (DeepMind) · Dilan Gorur () · Chris Harris (Google) · Dale Schuurmans (Google Brain & University of Alberta)

43. Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

Hongseok Namkoong (Stanford University) · Ramtin Keramati (Stanford University) · Steve Yadlowsky (Stanford University) · Emma Brunskill (Stanford University)

44. Self-Imitation Learning via Generalized Lower Bound Q-learning

Yunhao Tang (Columbia University)

45. Weakly-Supervised Reinforcement Learning for Controllable Behavior

Lisa Lee (CMU / Google Brain / Stanford) · Ben Eysenbach (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Shixiang (Shane) Gu (Google Brain) · Chelsea Finn (Stanford)

46. An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods

Yanli Liu (UCLA) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Tamer Basar (University of Illinois at Urbana-Champaign) · Wotao Yin (Alibaba US, DAMO Academy)

47. MOReL: Model-Based Offline Reinforcement Learning

Rahul Kidambi (Cornell University) · Aravind Rajeswaran (University of Washington) · Praneeth Netrapalli (Microsoft Research) · Thorsten Joachims (Cornell)

48. Zap Q-Learning With Nonlinear Function Approximation

Shuhang Chen (University of Florida) · Adithya M Devraj (University of Florida) · Fan Lu (University of Florida) · Ana Busic (INRIA) · Sean Meyn (University of Florida)

49. Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension

Ruosong Wang (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Lin Yang (UCLA)

50. Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms

Pinar Ozisik (UMass Amherst) · Philip Thomas (University of Massachusetts Amherst)

51. RepPoints v2: Verification Meets Regression for Object Detection

Yihong Chen (Peking University) · Zheng Zhang (MSRA) · Yue Cao (Microsoft Research) · Liwei Wang (Peking University) · Stephen Lin (Microsoft Research) · Han Hu (Microsoft Research Asia)

52. Learning to Communicate in Multi-Agent Systems via Transformer-Guided Program Synthesis

Jeevana Priya Inala (MIT) · Yichen Yang (MIT) · James Paulos (University of Pennsylvania) · Yewen Pu (MIT) · Osbert Bastani (University of Pennysylvania) · Vijay Kumar (University of Pennsylvania) · Martin Rinard (MIT) · Armando Solar-Lezama (MIT)

53. Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information

Genevieve E Flaspohler (Massachusetts Institute of Technology) · Nicholas Roy (MIT) · John W Fisher III (MIT)

54. Bayesian Multi-type Mean Field Multi-agent Imitation Learning

Fan Yang (University at Buffalo) · Alina Vereshchaka (University at Buffalo) · Changyou Chen (University at Buffalo) · Wen Dong (University at Buffalo)

55. Model-based Adversarial Meta-Reinforcement Learning

Zichuan Lin (Tsinghua University) · Garrett W. Thomas (Stanford University) · Guangwen Yang (Tsinghua University) · Tengyu Ma (Stanford University)

56. Provably Efficient Neural GTD for Off-Policy Learning

Hoi-To Wai (The Chinese University of Hong Kong) · Zhuoran Yang (Princeton) · Zhaoran Wang (Northwestern University) · Mingyi Hong (University of Minnesota)

57. A Randomized Algorithm to Reduce the Support of Discrete Measures

Francesco Cosentino (University of Oxford) · Harald Oberhauser (University of Oxford) · Alessandro Abate (University of Oxford)

58. Model Inversion Networks for Model-Based Optimization

Aviral Kumar (UC Berkeley) · Sergey Levine (UC Berkeley)

59. Safe Reinforcement Learning via Curriculum Induction

Matteo Turchetta (ETH Zurich) · Andrey Kolobov (Microsoft Research) · Shital Shah (Microsoft) · Andreas Krause (ETH Zurich) · Alekh Agarwal (Microsoft Research)

60. Conservative Q-Learning for Offline Reinforcement Learning

Aviral Kumar (UC Berkeley) · Aurick Zhou (University of California, Berkeley) · George Tucker (Google Brain) · Sergey Levine (UC Berkeley)

61. SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection

Xiaoya Li (Shannon.AI) · Yuxian Meng (Shannon.AI) · Mingxin Zhou (Shannon.AI) · Qinghong Han (Shannon.AI) · Fei Wu (Zhejiang University) · Jiwei Li (Shannon.AI)

62. Variational Bayesian Monte Carlo with Noisy Likelihoods

Luigi Acerbi (University of Helsinki)

63. Munchausen Reinforcement Learning

Nino Vieillard (Google Brain) · Olivier Pietquin (Google Research Brain Team) · Matthieu Geist (Google Brain)

64. A Self-Tuning Actor-Critic Algorithm

Tom Zahavy (Technion) · Zhongwen Xu (DeepMind) · Vivek Veeriah (University of Michigan) · Matteo Hessel (Google DeepMind) · Junhyuk Oh (DeepMind) · Hado van Hasselt (DeepMind) · David Silver (DeepMind) · Satinder Singh (DeepMind)

65. Non-Crossing Quantile Regression for Distributional Reinforcement Learning

Fan Zhou (Shanghai University of Finance and Economics) · Jianing Wang (Shanghai University of Finance and Economics) · Xingdong Feng (Shanghai University of Finance and Economics)

66. Learning Implicit Credit Assignment for Multi-Agent Actor-Critic

Meng Zhou (University of Sydney) · Ziyu Liu (University of Sydney) · Pengwei Sui (University of Sydney) · Yixuan Li (The University of Sydney) · Yuk Ying Chung (The University of Sydney)

67. Online Meta-Critic Learning for Off-Policy Actor-Critic Methods

Wei Zhou (National University of Defense Technology) · Yiying Li (National University of Defense Technology) · Yongxin Yang (University of Edinburgh ) · Huaimin Wang (National University of Defense Technology) · Timothy Hospedales (University of Edinburgh)

68. Online Decision Based Visual Tracking via Reinforcement Learning

ke Song (Shandong university) · Wei Zhang (Shandong University) · Ran Song (School of Control Science and Engineering, Shandong University) · Yibin Li (Shandong University)

69. Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Paul Barde (Quebec AI institute - Ubisoft La Forge) · Julien Roy (Mila) · Wonseok Jeon (MILA, McGill University) · Joelle Pineau (McGill University) · Chris Pal (MILA, Polytechnique MontrÃ©al, Element AI) · Derek Nowrouzezahrai (McGill University)

70. Discovering Reinforcement Learning Algorithms

Junhyuk Oh (DeepMind) · Matteo Hessel (Google DeepMind) · Wojciech Czarnecki (DeepMind) · Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

71. Model-based Policy Optimization with Unsupervised Model Adaptation

Jian Shen (Shanghai Jiao Tong University) · Han Zhao (Carnegie Mellon University) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong Unviersity)

72. Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

Filippos Christianos (University of Edinburgh) · Lukas SchÃ¤fer (University of Edinburgh) · Stefano Albrecht (University of Edinburgh)

73. The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Harm Van Seijen (Microsoft Research) · Hadi Nekoei (MILA) · Evan Racah (Mila, UniversitÃ© de MontrÃ©al) · Sarath Chandar (Mila / Ã‰cole Polytechnique de MontrÃ©al)

74. Deep Inverse Q-learning with Constraints

Gabriel Kalweit (University of Freiburg) · Maria Huegle (University of Freiburg) · Moritz Werling (BMWGroup, Unterschleissheim) · Joschka Boedecker (University of Freiburg)

75. Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning

Nino Vieillard (Google Brain) · Tadashi Kozuno (Okinawa Institute of Science and Technology) · Bruno Scherrer (INRIA) · Olivier Pietquin (Google Research Brain Team) · Remi Munos (DeepMind) · Matthieu Geist (Google Brain)

76. Task-agnostic Exploration in Reinforcement Learning

Xuezhou Zhang (UW-Madison) · Yuzhe Ma (University of Wisconsin-Madison) · Adish Singla (MPI-SWS)

77. Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

Tianren Zhang (Tsinghua University) · Shangqi Guo (Tsinghua University) · Tian Tan (Stanford University) · Xiaolin Hu (Tsinghua University) · Feng Chen (Tsinghua University)

78. Reinforcement Learning with Feedback Graphs

Christoph Dann (Carnegie Mellon University) · Yishay Mansour (Google) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Ayush Sekhari (Cornell University) · Karthik Sridharan (Cornell University)

79. Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning

Jianda Chen (Nanyang Technological University) · Shangyu Chen (Nanyang Technological University, Singapore) · Sinno Jialin Pan (Nanyang Technological University, Singapore)

80. Towards Safe Policy Improvement for Non-Stationary MDPs

Yash Chandak (University of Massachusetts Amherst) · Scott Jordan (University of Massachusetts Amherst) · Georgios Theocharous (Adobe Research) · Martha White (University of Alberta) · Philip Thomas (University of Massachusetts Amherst)

81. Multi-Task Reinforcement Learning with Soft Modularization

Ruihan Yang (UC San Diego) · Huazhe Xu (UC Berkeley) · YI WU (UC Berkeley) · Xiaolong Wang (UCSD/UC Berkeley)

82. Weighted QMIX: Improving Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid (University of Oxford) · Gregory Farquhar (University of Oxford) · Bei Peng (University of Oxford) · Shimon Whiteson (University of Oxford)

83. MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Elise van der Pol (University of Amsterdam) · Daniel Worrall (University of Amsterdam) · Herke van Hoof (University of Amsterdam) · Frans Oliehoek (TU Delft) · Max Welling (University of Amsterdam / Qualcomm AI Research)

84. CoinDICE: Off-Policy Confidence Interval Estimation

Bo Dai (Google Brain) · Ofir Nachum (Google Brain) · Yinlam Chow (Google Research) · Lihong Li (Google Research) · Csaba Szepesvari (DeepMind / University of Alberta) · Dale Schuurmans (Google Brain & University of Alberta)

85. An Operator View of Policy Gradient Methods

Dibya Ghosh (Google) · Marlos C. Machado (Google Brain) · Nicolas Le Roux (Google Brain)

86. On Efficiency in Hierarchical Reinforcement Learning

Zheng Wen (DeepMind) · Doina Precup (DeepMind) · Morteza Ibrahimi (DeepMind) · Andre Barreto (DeepMind) · Benjamin Van Roy (Stanford University) · Satinder Singh (DeepMind)

87. Variational Policy Gradient Method for Reinforcement Learning with General Utilities

Junyu Zhang (Princeton University) · Alec Koppel (U.S. Army Research Laboratory) · Amrit Singh Bedi (US Army Research Laboratory) · Csaba Szepesvari (DeepMind / University of Alberta) · Mengdi Wang (Princeton University)

88. A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods

Yue Wu (University of California, Los Angeles) · Weitong ZHANG (University of California, Los Angeles) · Pan Xu (University of California, Los Angeles) · Quanquan Gu (UCLA)

89. POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis

Weichao Mao (University of Illinois Urbana-Champaign) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Qiaomin Xie (Cornell University) · Tamer Basar (University of Illinois at Urbana-Champaign)

90. Can Temporal-Diï¬€erence and Q-Learning Learn Representation? A Mean-Field Theory

Yufeng Zhang (Northwestern University) · Qi Cai (Northwestern University) · Zhuoran Yang (Princeton) · Yongxin Chen (Georgia Institute of Technology) · Zhaoran Wang (Northwestern University)

91. Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

Jianzhun Du (Harvard University) · Joseph Futoma (Harvard University) · Finale Doshi-Velez (Harvard)

92. Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction

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

93. Reinforcement Learning with Augmented Data

Misha Laskin (UC Berkeley) · Kimin Lee (UC Berkeley) · Adam Stooke (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai) · Aravind Srinivas (UC Berkeley)

94. Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

Jean Tarbouriech (Facebook AI Research Paris & Inria Lille) · Matteo Pirotta (Facebook AI Research) · Michal Valko (DeepMind Paris and Inria Lille - Nord Europe) · Alessandro Lazaric (Facebook Artificial Intelligence Research)

95. EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning

Jiachen Li (University of California, Berkeley) · Fan Yang (University of California, Berkeley) · Masayoshi Tomizuka (University of California, Berkeley) · Chiho Choi (Honda Research Institute US)

96. Autofocused oracles for model-based design

Clara Fannjiang (UC Berkeley) · Jennifer Listgarten (UC Berkeley)

97. Off-Policy Evaluation via the Regularized Lagrangian

Mengjiao Yang (Google) · Ofir Nachum (Google Brain) · Bo Dai (Google Brain) · Lihong Li (Google Research) · Dale Schuurmans (Google Brain & University of Alberta)

98. Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

Arthur Delarue (MIT) · Ross Anderson (Google Research) · Christian Tjandraatmadja (Google)

99. MOPO: Model-based Offline Policy Optimization

Tianhe Yu (Stanford University) · Garrett W. Thomas (Stanford University) · Lantao Yu (Stanford University) · Stefano Ermon (Stanford) · James Zou (Stanford University) · Sergey Levine (UC Berkeley) · Chelsea Finn (Stanford) · Tengyu Ma (Stanford University)

100. Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis

Shaocong Ma (University of Utah) · Yi Zhou (University of Utah) · Shaofeng Zou (University at Buffalo, the State University of New York)

101. Dynamic Regret of Policy Optimization in Non-stationary Environments

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

102. DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

Aviral Kumar (UC Berkeley) · Abhishek Gupta (University of California, Berkeley) · Sergey Levine (UC Berkeley)

103. FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

Alekh Agarwal (Microsoft Research) · Sham Kakade (University of Washington) · Akshay Krishnamurthy (Microsoft) · Wen Sun (Microsoft Research NYC)

104. Neurosymbolic Reinforcement Learning with Formally Verified Exploration

Greg Anderson (University of Texas at Austin) · Abhinav Verma (Rice University) · Isil Dillig (UT Austin) · Swarat Chaudhuri (The University of Texas at Austin)

105. Generalized Hindsight for Reinforcement Learning

Alexander Li (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai)

106. Finite-Time Analysis for Double Q-learning

Huaqing Xiong (Ohio State University) · Lin Zhao (National University of Singapore) · Yingbin Liang (The Ohio State University) · Wei Zhang (Southern University of Science and Technology)

107. Subgroup-based Rank-1 Lattice Quasi-Monte Carlo

Yueming LYU (University of Technology Sydney) · Yuan Yuan (MIT) · Ivor Tsang (University of Technology, Sydney)

108. Meta-Gradient Reinforcement Learning with an Objective Discovered Online

Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Matteo Hessel (Google DeepMind) · Junhyuk Oh (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

109. TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

Tarun Gogineni (University of Michigan) · Ziping Xu (University of Michigan) · Exequiel Punzalan (University of Michigan) · Runxuan Jiang (University of Michigan) · Joshua Kammeraad (University of Michigan) · Ambuj Tewari (University of Michigan) · Paul Zimmerman (University of Michigan)

110. Succinct and Robust Multi-Agent Communication With Temporal Message Control

Sai Qian Zhang (Harvard University) · Qi Zhang (Amazon) · Jieyu Lin (University of Toronto)

111. Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Cong Zhang (Nanyang Technological University) · Wen Song (Institute of Marine Scinece and Technology, Shandong University) · Zhiguang Cao (National University of Singapore) · Jie Zhang (Nanyang Technological University) · Puay Siew Tan (SIMTECH) · Xu Chi (Singapore Institute of Manufacturing Technology, A-Star)

112. Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?

Qiwen Cui (Peking University) · Lin Yang (UCLA)

113. Instance-based Generalization in Reinforcement Learning

Martin Bertran (Duke University) · Natalia L Martinez (Duke University) · Mariano Phielipp (Intel AI Labs) · Guillermo Sapiro (Duke University)

114. Preference-based Reinforcement Learning with Finite-Time Guarantees

Yichong Xu (Carnegie Mellon University) · Ruosong Wang (Carnegie Mellon University) · Lin Yang (UCLA) · Aarti Singh (CMU) · Artur Dubrawski (Carnegie Mellon University)

115. Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes

Salman Habib (New Jersey Institute of Tech) · Allison Beemer (New Jersey Institute of Technology) · Joerg Kliewer (New Jersey Institute of Technology)

116. BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

Xinyue Chen (NYU Shanghai) · Zijian Zhou (NYU Shanghai) · Zheng Wang (NYU Shanghai) · Che Wang (New York University) · Yanqiu Wu (New York University) · Keith Ross (NYU Shanghai)

117. Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Mengdi Xu (Carnegie Mellon University) · Wenhao Ding (Carnegie Mellon University) · Jiacheng Zhu (Carnegie Mellon University) · ZUXIN LIU (Carnegie Mellon University) · Baiming Chen (Tsinghua University) · Ding Zhao (Carnegie Mellon University)

118. On Reward-Free Reinforcement Learning with Linear Function Approximation

Ruosong Wang (Carnegie Mellon University) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA) · Russ Salakhutdinov (Carnegie Mellon University)

119. Near-Optimal Reinforcement Learning with Self-Play

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

120. Robust Multi-Agent Reinforcement Learning with Model Uncertainty

Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · TAO SUN (Amazon.com) · Yunzhe Tao (Amazon Artificial Intelligence) · Sahika Genc (Amazon Artificial Intelligence) · Sunil Mallya (Amazon AWS) · Tamer Basar (University of Illinois at Urbana-Champaign)

121. Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes

Yi Tian (MIT) · Jian Qian (MIT) · Suvrit Sra (MIT)

122. Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward

Guannan Qu (California Institute of Technology) · Yiheng Lin (California Institute of Technology) · Adam Wierman (California Institute of Technology) · Na Li (Harvard University)

123. Constrained episodic reinforcement learning in concave-convex and knapsack settings

KiantÃ© Brantley (The University of Maryland College Park) · Miro Dudik (Microsoft Research) · Thodoris Lykouris (Microsoft Research NYC) · Sobhan Miryoosefi (Princeton University) · Max Simchowitz (Berkeley) · Aleksandrs Slivkins (Microsoft Research) · Wen Sun (Microsoft Research NYC)

124. Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation

Devavrat Shah (Massachusetts Institute of Technology) · Dogyoon Song (Massachusetts Institute of Technology) · Zhi Xu (MIT) · Yuzhe Yang (MIT)

125. Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Younggyo Seo (KAIST) · Kimin Lee (UC Berkeley) · Ignasi Clavera Gilaberte (UC Berkeley) · Thanard Kurutach (University of California Berkeley) · Jinwoo Shin (KAIST) · Pieter Abbeel (UC Berkeley & covariant.ai)

126. Cooperative Heterogeneous Deep Reinforcement Learning

Han Zheng (UTS) · Pengfei Wei (National University of Singapore) · Jing Jiang (University of Technology Sydney) · Guodong Long (University of Technology Sydney (UTS)) · Qinghua Lu (Data61, CSIRO) · Chengqi Zhang (University of Technology Sydney)

127. Global Convergence of Natural Primal-Dual Method for Constrained Markov Decision Processes

Dongsheng Ding (University of Southern California) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Mihailo Jovanovic (University of Southern California) · Tamer Basar (University of Illinois at Urbana-Champaign)

128. Implicit Distributional Reinforcement Learning

Yuguang Yue (University of Texas at Austin) · Zhendong Wang (University of Texas, Austin) · Mingyuan Zhou (University of Texas at Austin)

129. Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

Sreejith Balakrishnan (National University of Singapore) · Quoc Phong Nguyen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Harold Soh (National University Singapore)

130. EPOC: A Provably Correct Policy Gradient Approach to Reinforcement Learning

Alekh Agarwal (Microsoft Research) · Mikael Henaff (Microsoft) · Sham Kakade (University of Washington) · Wen Sun (Microsoft Research NYC)

131. Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations

Zhuoran Yang (Princeton) · Chi Jin (Princeton University) · Zhaoran Wang (Northwestern University) · Mengdi Wang (Princeton University) · Michael Jordan (UC Berkeley)

132. Decoupled Policy Gradient Methods for Competitive Reinforcement Learning

Constantinos Daskalakis (MIT) · Dylan Foster (MIT) · Noah Golowich (Massachusetts Institute of Technology)

133. Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss

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

134. Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Sham Kakade (University of Washington) · Tamer Basar (University of Illinois at Urbana-Champaign) · Lin Yang (UCLA)

135. PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

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

136. Improving Generalization in Reinforcement Learning with Mixture Regularization

KAIXIN WANG (National University of Singapore) · Bingyi Kang (National University of Singapore) · Jie Shao (Fudan University) · Jiashi Feng (National University of Singapore)

137. A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning

Arnu Pretorius (InstaDeep) · Scott Cameron (Instadeep) · Elan van Biljon (Stellenbosch University) · Thomas Makkink (InstaDeep) · Shahil Mawjee (InstaDeep) · Jeremy du Plessis (University of Cape Town) · Jonathan Shock (University of Cape Town) · Alexandre Laterre (InstaDeep) · Karim Beguir (InstaDeep)

138. Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint

Georgios Amanatidis (University of Essex) · Federico Fusco (Sapienza University of Rome) · Philip Lazos (Sapienza University of Rome) · Stefano Leonardi (Sapienza University of Rome) · Rebecca ReiffenhÃ¤user (Sapienza University of Rome)

139. Planning in Markov Decision Processes with Gap-Dependent Sample Complexity

Anders Jonsson (Universitat Pompeu Fabra) · Emilie Kaufmann (CNRS) · Pierre Menard (Inria) · Omar Darwiche Domingues (Inria) · Edouard Leurent (INRIA) · Michal Valko (DeepMind)

140. Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

Yunqiu Xu (University of Technology Sydney) · Meng Fang (Tencent) · Ling Chen (" University of Technology, Sydney, Australia") · Yali Du (University College London) · Joey Tianyi Zhou (IHPC, A*STAR) · Chengqi Zhang (University of Technology Sydney)

141. Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Parameswaran Kamalaruban (EPFL) · Yu-Ting Huang (EPFL) · Ya-Ping Hsieh (EPFL) · Paul Rolland (EPFL) · Cheng Shi (Unversity of Basel) · Volkan Cevher (EPFL)

142. Interferobot: aligning an optical interferometer by a reinforcement learning agent

Dmitry Sorokin (Russian Quantum Center) · Alexander Ulanov (Russian Quantum Center) · Ekaterina Sazhina (Russian Quantum Center) · Alexander Lvovsky (Oxford University)

143. Reinforcement Learning for Control with Multiple Frequencies

Jongmin Lee (KAIST) · ByungJun Lee (KAIST) · Kee-Eung Kim (KAIST)

144. Learning to Play Sequential Games versus Unknown Opponents

Pier Giuseppe Sessa (ETH ZÃ¼rich) · Ilija Bogunovic (ETH Zurich) · Maryam Kamgarpour (ETH ZÃ¼rich) · Andreas Krause (ETH Zurich)

145. Contextual Games: Multi-Agent Learning with Side Information

Pier Giuseppe Sessa (ETH ZÃ¼rich) · Ilija Bogunovic (ETH Zurich) · Andreas Krause (ETH Zurich) · Maryam Kamgarpour (ETH ZÃ¼rich)

146. Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton) · Yudong Chen (Cornell University) · Zhaoran Wang (Northwestern University) · Qiaomin Xie (Cornell University)

147. Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation

Aaron Sonabend (Harvard University) · Junwei Lu () · Leo Anthony Celi (Massachusetts Institute of Technology) · Tianxi Cai (Harvard School of Public Health) · Peter Szolovits (MIT)

148. Dynamic allocation of limited memory resources in reinforcement learning

Nisheet Patel (University of Geneva) · Luigi Acerbi (University of Helsinki) · Alexandre Pouget (University of Geneva)

149. AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

Afshin Oroojlooy (SAS Institute, Inc) · Mohammadreza Nazari (SAS Institute Inc.) · Davood Hajinezhad (SAS Institute Inc.) · Jorge Silva (SAS)

150. Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Chi Jin (Princeton University) · Sham Kakade (University of Washington) · Akshay Krishnamurthy (Microsoft) · Qinghua Liu (Princeton University)

151. Learning discrete distributions with infinite support

Doron Cohen (Ben-Gurion University of the Negev) · Aryeh Kontorovich (Ben Gurion University) · Geoï¬€rey Wolfer (Ben-Gurion University of the Negev)

152. Joint Policy Search for Multi-agent Collaboration with Incomplete Information

Yuandong Tian (Facebook AI Research) · Qucheng Gong (Facebook AI Research) · Yu Jiang (Facebook AI Research)

153. R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making

Sergey Shuvaev (Cold Spring Harbor Laboratory) · Sarah Starosta (Washington University in St. Louis) · Duda Kvitsiani (Aarhus University) · Adam Kepecs (Washington University in St. Louis) · Alexei Koulakov (Cold Spring Harbor Laboratory)

154. Multi-agent active perception with prediction rewards

Mikko Lauri (University of Hamburg) · Frans Oliehoek (TU Delft)

155. RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning

Ziyu Wang (Deepmind) · Caglar Gulcehre (Deepmind) · Alexander Novikov (DeepMind) · Thomas Paine (DeepMind) · Sergio GÃ³mez (DeepMind) · Konrad Zolna (DeepMind) · Rishabh Agarwal (Google Research, Brain Team) · Josh Merel (DeepMind) · Daniel Mankowitz (DeepMind) · Cosmin Paduraru (DeepMind) · Gabriel Dulac-Arnold (Google Research) · Jerry Li (Google) · Mohammad Norouzi (Google Brain) · Matthew Hoffman (DeepMind) · Nicolas Heess (Google DeepMind) · Nando de Freitas (DeepMind)

156. A local temporal difference code for distributional reinforcement learning

Pablo Tano (University of Geneva) · Peter Dayan (Max Planck Institute for Biological Cybernetics) · Alexandre Pouget (University of Geneva)

157. Learning to Play No-Press Diplomacy with Best Response Policy Iteration

Thomas Anthony (DeepMind) · Tom Eccles (DeepMind) · Andrea Tacchetti (DeepMind) · JÃ¡nos KramÃ¡r (DeepMind) · Ian Gemp (DeepMind) · Thomas Hudson (DeepMind) · Nicolas Porcel (DeepMind) · Marc Lanctot (DeepMind) · Julien Perolat (DeepMind) · Richard Everett (DeepMind) · Satinder Singh (DeepMind) · Thore Graepel (DeepMind) · Yoram Bachrach ()

158. The Value Equivalence Principle for Model-Based Reinforcement Learning

Christopher Grimm (University of Michigan) · Andre Barreto (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

159. Multi-agent Trajectory Prediction with Fuzzy Query Attention

Nitin Kamra (University of Southern California) · Hao Zhu (Peking University) · Dweep Kumarbhai Trivedi (University of Southern California) · Ming Zhang (Peking University) · Yan Liu (University of Southern California)

160. Trust the Model When It Is Confident: Masked Model-based Actor-Critic

Feiyang Pan (Institute of Computing Technology, Chinese Academy of Sciences) · Jia He (Huawei) · Dandan Tu (Huawei) · Qing He (Institute of Computing Technology, Chinese Academy of Sciences)

161. POMDPs in Continuous Time and Discrete Spaces

Bastian Alt (Technische UniversitÃ¤t Darmstadt) · Matthias Schultheis (Technische UniversitÃ¤t Darmstadt) · Heinz Koeppl (Technische UniversitÃ¤t Darmstadt)

162. Steady State Analysis of Episodic Reinforcement Learning

Huang Bojun (Rakuten Institute of Technology)

163. Learning Multi-Agent Communication through Structured Attentive Reasoning

Murtaza Rangwala (Virginia Tech) · Ryan K Williams (Virginia Tech)

164. Information-theoretic Task Selection for Meta-Reinforcement Learning

Ricardo Luna Gutierrez (University of Leeds) · Matteo Leonetti (University of Leeds)

165. The Mean-Squared Error of Double Q-Learning

Wentao Weng (Tsinghua University) · Harsh Gupta (University of Illinois at Urbana-Champaign) · Niao He (UIUC) · Lei Ying (University of Michigan) · R. Srikant (University of Illinois at Urbana-Champaign)

166. A Unifying View of Optimism in Episodic Reinforcement Learning

Gergely Neu (Universitat Pompeu Fabra) · Ciara Pike-Burke (Imperial College London)

167. Accelerating Reinforcement Learning through GPU Atari Emulation

Steven Dalton (Nvidia) · iuri frosio (nvidia)

168. Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

Huan Zhang (UCLA) · Hongge Chen (MIT) · Chaowei Xiao (University of Michigan, Ann Arbor) · Bo Li (UIUC) · mingyan liu (university of Michigan, Ann Arbor) · Duane Boning (Massachusetts Institute of Technology) · Cho-Jui Hsieh (UCLA)

169. Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

Guangxiang Zhu (Tsinghua university) · Minghao Zhang (Tsinghua University) · Honglak Lee (Google / U. Michigan) · Chongjie Zhang (Tsinghua University)

170. Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces

Guy Lorberbom (Technion) · Chris J. Maddison (University of Toronto) · Nicolas Heess (Google DeepMind) · Tamir Hazan (Technion) · Daniel Tarlow (Google Brain)

171. Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation

Charles Margossian (Columbia) · Aki Vehtari (Aalto University) · Daniel Simpson (University of Toronto) · Raj Agrawal (MIT)

172. A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms

Niao He (UIUC) · Donghwan Lee (KAIST)

173. Adaptive Discretization for Model-Based Reinforcement Learning

Sean Sinclair (Cornell University) · Tianyu Wang (Duke University) · Gauri Jain (Cornell University) · Siddhartha Banerjee (Cornell University) · Christina Yu (Cornell University)

174. Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes

Yuval Emek (Technion - Israel Institute of Technology) · Ron Lavi (Technion) · Rad Niazadeh (Chicago Booth School of Business) · Yangguang Shi (Technion - Israel Institute of Technology)

175. Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration

Yao Liu (Stanford University) · Adith Swaminathan (Microsoft Research) · Alekh Agarwal (Microsoft Research) · Emma Brunskill (Stanford University)

176. Off-Policy Interval Estimation with Lipschitz Value Iteration

Ziyang Tang (UT Austin) · Yihao Feng (UT Austin) · Na Zhang (Tsinghua University) · Jian Peng (University of Illinois at Urbana-Champaign) · Qiang Liu (UT Austin)

177. Provably adaptive reinforcement learning in metric spaces

Tongyi Cao (University of Massachusetts Amherst) · Akshay Krishnamurthy (Microsoft)

178. Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Alex Lee (UC Berkeley) · Anusha Nagabandi (UC Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai) · Sergey Levine (UC Berkeley)

179. Inverse Reinforcement Learning from a Gradient-based Learner

Giorgia Ramponi (Politecnico di Milano) · Gianluca Drappo (Politecnico di Milano) · Marcello Restelli (Politecnico di Milano)

180. Efficient Planning in Large MDPs with Weak Linear Function Approximation

Roshan Shariff (University of Alberta) · Csaba Szepesvari (DeepMind / University of Alberta)