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 Técnico)
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 Ré (Stanford)
26. Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Julien Roy (Mila) · Paul Barde (Quebec AI institute - Ubisoft La Forge) · Félix G Harvey (Polytechnique Montréal) · Derek Nowrouzezahrai (McGill University) · Chris Pal (MILA, Polytechnique Montréal, 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 Zürich) · 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 Montréal, 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 Schäfer (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, Université de Montréal) · Sarath Chandar (Mila / École Polytechnique de Montréal)
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-Difference 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
Kianté 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 Reiffenhäuser (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 Zürich) · Ilija Bogunovic (ETH Zurich) · Maryam Kamgarpour (ETH Zürich) · Andreas Krause (ETH Zurich)
145. Contextual Games: Multi-Agent Learning with Side Information
Pier Giuseppe Sessa (ETH Zürich) · Ilija Bogunovic (ETH Zurich) · Andreas Krause (ETH Zurich) · Maryam Kamgarpour (ETH Zürich)
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) · Geoffrey 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 Gómez (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) · János Kramár (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 Universität Darmstadt) · Matthias Schultheis (Technische Universität Darmstadt) · Heinz Koeppl (Technische Universität 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)
