【重磅整理】180篇NIPS-2020顶会《强化学习领域》Accept论文大全

深度强化学习实验室

作者：《 DeepRL-Lab》 & 《AMiner.cn》联合发布

来源：https://neurips.cc/Conferences/2020/

编辑：DeepRL

（图片来自新智元）

NeurIPS终于放榜，提交数再次创新高，与去年相比增加了38%，共计达到9454篇，总接收1900篇，其中谷歌以169篇傲视群雄，清华大学63篇，南京大学周志华教授团队3篇。论文接收率20.09%较去年有所下降，其中论文主题占比和结构图如下：

• 算法（29%）

• 深度学习（19%）

• 强化学习（9%）

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强化学习完整列表

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

作者: 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

作者: 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

作者: 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

作者: 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

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

[6]. Off-Policy Imitation Learning from Observations

作者: 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?

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

[8]. DISK: Learning local features with policy gradient

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

[9]. Learning Individually Inferred Communication for Multi-Agent Cooperation

作者: 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

作者: 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

作者: 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

作者: 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

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

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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[25]. HiPPO: Recurrent Memory with Optimal Polynomial Projections

作者: 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

作者: 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

作者: 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

作者: 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

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

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

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

[31]. Learning Retrospective Knowledge with Reverse Reinforcement Learning

作者: 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

作者: 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

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

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

作者: 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

作者: Guangyao Zhou (Vicarious AI)

[36]. Self-Paced Deep Reinforcement Learning

作者: 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

作者: 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

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

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

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

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

作者: 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

作者: 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

作者: 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

作者: 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

作者: Yunhao Tang (Columbia University)

[45]. Weakly-Supervised Reinforcement Learning for Controllable Behavior

作者: 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

作者: 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

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

[48]. Zap Q-Learning With Nonlinear Function Approximation

作者: 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

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

[50]. Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms

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

[51]. RepPoints v2: Verification Meets Regression for Object Detection

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[58]. Model Inversion Networks for Model-Based Optimization

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

[59]. Safe Reinforcement Learning via Curriculum Induction

作者: 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

作者: 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

作者: 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

作者: Luigi Acerbi (University of Helsinki)

[63]. Munchausen Reinforcement Learning

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

[64]. A Self-Tuning Actor-Critic Algorithm

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[77]. Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[86]. On Efficiency in Hierarchical Reinforcement Learning

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

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

作者: 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

作者: 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

作者: 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

作者: 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

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

[97]. Off-Policy Evaluation via the Regularized Lagrangian

作者: 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

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

[99]. MOPO: Model-based Offline Policy Optimization

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[106]. Finite-Time Analysis for Double Q-learning

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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?

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

[113]. Instance-based Generalization in Reinforcement Learning

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[120]. Robust Multi-Agent Reinforcement Learning with Model Uncertainty

作者: 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

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

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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

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

作者: 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

作者: 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

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

[136]. Improving Generalization in Reinforcement Learning with Mixture Regularization

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[144]. Learning to Play Sequential Games versus Unknown Opponents

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

作者: 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

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

[151]. Learning discrete distributions with infinite support

作者: 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

作者: 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

作者: 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

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

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

作者: 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

作者: 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

作者: 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

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

[159]. Multi-agent Trajectory Prediction with Fuzzy Query Attention

作者: 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

作者: 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

作者: Bastian Alt (Technische Universität Darmstadt) · Matthias Schultheis (Technische Universität Darmstadt) · Heinz Koeppl (Technische Universität Darmstadt)

[162]. Steady State Analysis of Episodic Reinforcement Learning

作者: Huang Bojun (Rakuten Institute of Technology)

[163]. Learning Multi-Agent Communication through Structured Attentive Reasoning

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

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

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

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

作者: 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

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

[167]. Accelerating Reinforcement Learning through GPU Atari Emulation

作者: Steven Dalton (Nvidia) · iuri frosio (nvidia)

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

作者: 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

作者: 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

作者: 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

作者: 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

作者: Niao He (UIUC) · Donghwan Lee (KAIST)

[173]. Adaptive Discretization for Model-Based Reinforcement Learning

作者: 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

作者: 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

作者: 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

作者: 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

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

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

作者: 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

作者: 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

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

参考文献：https://neurips.cc/Conferences/2020/

完

总结1：周志华 || AI领域如何做研究-写高水平论文

总结2：全网首发最全深度强化学习资料(永更)

总结3:  《强化学习导论》代码/习题答案大全

总结4：30+个必知的《人工智能》会议清单

总结5：2019年-57篇深度强化学习文章汇总

总结6:  万字总结 || 强化学习之路

总结7：万字总结 || 多智能体强化学习(MARL)大总结

总结8：经验 || 深度强化学习理论、模型及编码调参技巧

完

第82篇：强化学习需要批归一化(Batch Norm)吗？

第81篇：【综述】多智能体强化学习算法理论研究

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第79篇: 诺亚方舟开源高性能强化学习库“刑天”

第78篇：强化学习如何tradeoff"探索"和"利用"？

第77篇：深度强化学习工程师/研究员面试指南

第76篇：DAI2020 自动驾驶挑战赛(强化学习)

第75篇：Distributional Soft Actor-Critic算法

第74篇：【中文公益公开课】RLChina2020

第73篇：Tensorflow2.0实现29种深度强化学习算法

第72篇：【万字长文】解决强化学习"稀疏奖励"

第71篇：【公开课】高级强化学习专题

第70篇：DeepMind发布"离线强化学习基准“

第69篇：深度强化学习【Seaborn】绘图方法

第68篇：【DeepMind】多智能体学习231页PPT

第67篇：126篇ICML2020会议"强化学习"论文汇总

第66篇：分布式强化学习框架Acme，并行性加强

第65篇：DQN系列(3): 优先级经验回放(PER)

第64篇：UC Berkeley开源RAD来改进强化学习算法

第63篇：华为诺亚方舟招聘 || 强化学习研究实习生

第62篇：ICLR2020- 106篇深度强化学习顶会论文

第61篇：David Sliver 亲自讲解AlphaGo、Zero

第60篇：滴滴主办强化学习挑战赛:KDD Cup-2020

第59篇：Agent57在所有经典Atari 游戏中吊打人类

第58篇：清华开源「天授」强化学习平台

第57篇：Google发布"强化学习"框架"SEED RL"

第56篇：RL教父Sutton实现强人工智能算法的难易

第55篇：内推 ||  阿里2020年强化学习实习生招聘

第54篇：顶会 || 65篇"IJCAI"深度强化学习论文

第53篇：TRPO/PPO提出者John Schulman谈科研

第52篇：《强化学习》可复现性和稳健性，如何解决？

第51篇：强化学习和最优控制的《十个关键点》

第50篇：微软全球深度强化学习开源项目开放申请

第49篇：DeepMind发布强化学习库 RLax

第48篇：AlphaStar过程详解笔记

第47篇：Exploration-Exploitation难题解决方法

第46篇：DQN系列(2): Double DQN 算法

第45篇：DQN系列(1): Double Q-learning

第44篇：科研界最全工具汇总

第43篇：起死回生|| 如何rebuttal顶会学术论文?

第42篇：深度强化学习入门到精通资料综述

第41篇：顶会征稿 ||  ICAPS2020: DeepRL

第40篇：实习生招聘 || 华为诺亚方舟实验室

第39篇：滴滴实习生|| 深度强化学习方向

第38篇：AAAI-2020 || 52篇深度强化学习论文

第37篇：Call For Papers# IJCNN2020-DeepRL

第36篇：复现"深度强化学习"论文的经验之谈

第35篇：α-Rank算法之DeepMind及Huawei改进

第34篇：从Paper到Coding, DRL挑战34类游戏

第33篇：DeepMind-102页深度强化学习PPT

第32篇：腾讯AI Lab强化学习招聘(正式/实习)

第31篇：强化学习，路在何方？

第30篇：强化学习的三种范例

第29篇：框架ES-MAML：进化策略的元学习方法

第28篇：138页“策略优化”PPT--Pieter Abbeel

第27篇：迁移学习在强化学习中的应用及最新进展

第26篇：深入理解Hindsight Experience Replay

第25篇：10项【深度强化学习】赛事汇总

第24篇：DRL实验中到底需要多少个随机种子？

第23篇：142页"ICML会议"强化学习笔记

第22篇：通过深度强化学习实现通用量子控制

第21篇：《深度强化学习》面试题汇总

第20篇：《深度强化学习》招聘汇总(13家企业）

第19篇：解决反馈稀疏问题之HER原理与代码实现

第18篇："DeepRacer" —顶级深度强化学习挑战赛

第17篇：AI Paper | 几个实用工具推荐

第16篇：AI领域：如何做优秀研究并写高水平论文？

第15篇： DeepMind开源三大新框架！

第14篇： 61篇NIPS2019DeepRL论文及部分解读

第13篇： OpenSpiel(28种DRL环境+24种DRL算法)

第12篇： 模块化和快速原型设计Huskarl DRL框架

第11篇： DRL在Unity自行车环境中配置与实践

第10篇： 解读72篇DeepMind深度强化学习论文

第9篇： 《AutoML》：一份自动化调参的指导

第8篇： ReinforceJS库（动态展示DP、TD、DQN）

第7篇： 10年NIPS顶会DRL论文(100多篇)汇总

第6篇： ICML2019-深度强化学习文章汇总

第5篇： 深度强化学习在阿里巴巴的技术演进

第4篇： 深度强化学习十大原则

第3篇： “超参数”自动化设置方法---DeepHyper

第2篇： 深度强化学习的加速方法

第1篇： 深入浅出解读"多巴胺（Dopamine）论文"、环境配置和实例分析

第14期论文:  2020-02-10(8篇)

第13期论文：2020-1-21(共7篇)

第12期论文：2020-1-10(Pieter Abbeel一篇,共6篇)

第11期论文：2019-12-19(3篇，一篇OpennAI)

第10期论文：2019-12-13(8篇)

第9期论文：2019-12-3(3篇)

第8期论文：2019-11-18(5篇)

第7期论文：2019-11-15(6篇)

第6期论文：2019-11-08(2篇)

第5期论文：2019-11-07(5篇，一篇DeepMind发表)

第4期论文：2019-11-05(4篇)

第3期论文：2019-11-04(6篇)

第2期论文：2019-11-03(3篇)

第1期论文：2019-11-02(5篇)