ICML2020 180篇强化学习论文汇总
强化学习小白一枚
之前有人已经整理过ICML2020 RL相关Paper，但昨天无意间发现有篇针对评论家高估问题的论文，没有被列出来，后面又无意间看了一篇和TRPO有关的似乎也遗漏了，所以就花了些精力看看自己整理整理论文，然后，发现大概有180篇，还是很多的。
这些论文根据自己的理解分了类（有可能有的分类有问题还请谅解，而且有接近一半还不太明确如何对它们分类比较合适。除此之外可能有的rl论文还是有遗漏，要么有可能有的误划入rl之中，还望以后有大佬能够进一步补充完善整理工作。。。）
很多论文arxiv上已经有了，相关论文链接暂时就不贴出来了，读者可以自行搜寻。
主要分类如下：
一、Model（主要对应Model-based RL)
二、Bandits（赌博机有关，大多涉及探索利用问题)
三、Exploration（不包含上面赌博机中的）
四、Batch RL
五、Imitation Learning
六、Multi-Agent RL
七、Multi-Objective RL
八、Policy Gradient（这里主要选的标题直接带策略梯度的）
九、Off-Policy Evaluation（异策略太多了，有好多可能不能单从标题看出来，时间有限就把其中典型的一个分支——OPE拿出来，其他论文暂时放在Other里面）
十、Application
十一、Other（其他的涵盖各个方面，比如safe RL，HRL，多任务RL，一些基础理论等等，但是有的领域本人不是太熟，有的论文划分感觉不明确，或者有的方面论文比较少等等，暂时就没有进行划分）
详细列表如下：
一、Model
- Active World Model Learning in Agent-rich Environments with Progress Curiosity
Kuno Kim (Stanford University) · Megumi Sano (Stanford University) · Julian De Freitas (Harvard University) · Nick Haber (Stanford University) · Daniel Yamins (Stanford University) - Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair (Stanford University) · Silvio Savarese (Stanford University) · Chelsea Finn (Stanford) - A Game Theoretic Perspective on Model-Based Reinforcement Learning
Aravind Rajeswaran (University of Washington) · Igor Mordatch (OpenAI) · Vikash Kumar (Google) - Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee (UC Berkeley) · Younggyo Seo (KAIST) · Seunghyun Lee (KAIST) · Honglak Lee (Google / U. Michigan) · Jinwoo Shin (KAIST) - A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change Salman Sadiq Shuvo (University of South Florida) · Yasin Yilmaz (University of South Florida) · Alan Bush (University of South Florida) · Mark Hafen (University of South Florida)
- Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Daniel Jarrett (University of Cambridge) · Mihaela van der Schaar (University of Cambridge) - Learning and Simulation in Generative Structured World Models
Zhixuan Lin (Zhejiang University) · Yi-Fu Wu (Rutgers University) · Skand Peri (Rutgers University, New Jersey) · Bofeng Fu (Tianjin University) · Jindong Jiang (Rutgers University) · Sungjin Ahn (Rutgers University) - Provably Efficient Model-based Policy Adaptation
Yuda Song (University of California, San Diego) · Aditi Mavalankar (University of California San Diego) · Wen Sun (Microsoft Research) · Sicun Gao (University of California, San Diego) - Selective Dyna-style Planning Under Limited Model Capacity
Muhammad Zaheer (University of Alberta) · Samuel Sokota (University of Alberta) · Erin Talvitie () · Martha White (University of Alberta) - Model-Based Reinforcement Learning with Value-Targeted Regression
Zeyu Jia (Peking University) · Lin Yang (UCLA) · Csaba Szepesvari (DeepMind/University of Alberta) · Mengdi Wang (Princeton University) · Alex Ayoub (University of Alberta) - Bidirectional Model-based Policy Optimization
Hang Lai (Shanghai Jiao Tong University) · Jian Shen (Shanghai Jiao Tong University) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong University) - Hallucinative Topological Memory for Zero-Shot Visual Planning
Thanard Kurutach (UC Berkeley) · Kara Liu (UC Berkeley) · Aviv Tamar (Technion) · Pieter Abbeel (UC Berkeley) · Christine Tung (UC Berkeley)
二、Bandits
- My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
Ilai Bistritz (Stanford University) · Tavor Z Baharav (Stanford University) · Amir Leshem (Bar-Ilan University) · Nicholas Bambos () - Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha (Indian Institute of Science (IISc), Bangalore) · Pierre Gaillard () · Michal Valko (DeepMind) - Multinomial Logit Bandit with Low Switching Cost
Kefan Dong (Tsinghua University) · Yingkai Li (Northwestern University) · Qin Zhang (Indiana University Bloomington) · Yuan Zhou (UIUC) - Optimistic Policy Optimization with Bandit Feedback
Lior Shani (Technion) · Yonathan Efroni (Technion) · Aviv Rosenberg (Tel Aviv University) · Shie Mannor (Technion) - Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
Chi Jin (Princeton University) · Tiancheng Jin (University of Southern California) · Haipeng Luo (University of Southern California) · Suvrit Sra (MIT) · Tiancheng Yu (MIT ) - Thompson Sampling Algorithms for Mean-Variance Bandits
Qiuyu Zhu (National University of Singapore) · Vincent Tan (National University of Singapore) - Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
Dylan Foster (MIT) · Alexander Rakhlin (MIT) - Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits
Xi Liu (Texas A&M University) · Ping-Chun Hsieh (National Chiao Tung University) · Yu Heng Hung (National Chiao Tung University) · Anirban Bhattacharya (Texas A&M University) · P. Kumar (Texas A&M University) - Non-Stationary Bandits with Intermediate Observations
Claire Vernade (DeepMind) · Andras Gyorgy (DeepMind) · Timothy Mann (DeepMind) - Linear bandits with Stochastic Delayed Feedback
Claire Vernade (DeepMind) · Alexandra Carpentier (Otto-von-Guericke University) · Tor Lattimore (DeepMind) · Giovanni Zappella (Amazon) · Beyza Ermis (Amazon Research) · Michael Brueckner (Amazon Research Berlin) - Improved Optimistic Algorithms for Logistic Bandits
Louis Faury (Criteo) · Marc Abeille (Criteo) · Clement Calauzenes (Criteo) · Olivier Fercoq (Telecom Paris) - Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou (UCLA) · Lihong Li (Google Research) · Quanquan Gu (University of California, Los Angeles) - Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Nian Si (Stanford University) · Fan Zhang (Stanford University) · Zhengyuan Zhou (Stanford University) · Jose Blanchet (Stanford University) - Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Lin Yang (UCLA) · Mengdi Wang (Princeton University) - Combinatorial Pure Exploration for Dueling Bandit
Wei Chen (Microsoft) · Yihan Du (IIIS, Tsinghua University) · Longbo Huang (Tsinghua University) · Haoyu Zhao (Tsinghua University) - The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng (Harvard University) · David Parkes (Harvard University) · Haifeng Xu (University of Virginia) - Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Vidyashankar Sivakumar (Walmart Labs) · Steven Wu (University of Minnesota) · Arindam Banerjee (University of Minnesota) - Gamification of Pure Exploration for Linear Bandits
Rémy Degenne (Inria) · Pierre Menard (Inria) · Xuedong Shang (Inria) · Michal Valko (DeepMind) - Structure Adaptive Algorithms for Stochastic Bandits
Rémy Degenne (Inria) · Han Shao (Toyota Technological Institute at Chicago) · Wouter Koolen (Centrum Wiskunde & Informatica, Amsterdam) - Meta-learning with Stochastic Linear Bandits
Leonardo Cella (University of Milan) · Alessandro Lazaric (Facebook AI Research) · Massimiliano Pontil (Istituto Italiano di Tecnologia and University College London) - Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Gellért Weisz (DeepMind) · Tor Lattimore (DeepMind) · Csaba Szepesvari (DeepMind/University of Alberta) - On conditional versus marginal bias in multi-armed bandits
Jaehyeok Shin (Carnegie Mellon University) · Aaditya Ramdas (Carnegie Mellon University) · Alessandro Rinaldo (Carnegie Mellon University) - Bandits for BMO Functions
Tianyu Wang (Duke University) · Cynthia Rudin (Duke)
三、Exploration
- Naive Exploration is Optimal for Online LQR
Max Simchowitz (UC Berkeley) · Dylan Foster (MIT) - Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff (Oregon State University) · Qinxun Bai (Horizon Robotics) · Fuxin Li (Oregon State University) · Wei Xu (Horizon Robotics) - No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech (Facebook AI Research Paris & Inria Lille) · Evrard Garcelon (Facebook AI Research ) · Michal Valko (DeepMind) · Matteo Pirotta (Facebook AI Research) · Alessandro Lazaric (Facebook AI Research) - Provably Efficient Exploration in Policy Optimization
Qi Cai (Northwestern University) · Zhuoran Yang (Princeton University) · Chi Jin (Princeton University) · Zhaoran Wang (Northwestern U) - Reward-Free Exploration for Reinforcement Learning
Chi Jin (Princeton University) · Akshay Krishnamurthy (Microsoft Research) · Max Simchowitz (UC Berkeley) · Tiancheng Yu (MIT ) - Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Marc Abeille (Criteo) · Alessandro Lazaric (Facebook AI Research) - Tightening Exploration in Upper Confidence Reinforcement Learning
Hippolyte Bourel (ENS Rennes) · Odalric-Ambrym Maillard (Inria Lille - Nord Europe) · Mohammad Sadegh Talebi (University of Copenhagen) - Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis (University of Toronto) · Harris Chan (University of Toronto, Vector Institute) · Stephen Zhao (University of Toronto) · Bradly Stadie (Vector Institute) · Jimmy Ba (University of Toronto) - Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis (Rice University) · Minjian Xin (Shanghai Jiao Tong University) · Dilip Arumugam (Stanford University) · Kevin Feigelis (Stanford University) · Daniel Yamins (Stanford University) - Planning to Explore via Latent Disagreement
Ramanan Sekar (University of Pennsylvania) · Oleh Rybkin (University of Pennsylvania / UC Berkeley (Visiting)) · Kostas Daniilidis (University of Pennsylvania) · Pieter Abbeel (UC Berkeley & Covariant) · Danijar Hafner (Google Brain & University of Toronto) · Deepak Pathak (UC Berkeley) - On Thompson Sampling with Langevin Algorithms
Eric Mazumdar (University of California Berkeley) · Aldo Pacchiano (UC Berkeley) · Yian Ma (Google) · Michael Jordan (UC Berkeley) · Peter Bartlett (UC Berkeley) - Thompson Sampling via Local Uncertainty
Zhendong Wang (University of Texas, Austin) · Mingyuan Zhou (University of Texas at Austin) - What Can Learned Intrinsic Rewards Capture?
Zeyu Zheng (University of Michigan) · Junhyuk Oh (DeepMind) · Matteo Hessel (Deep Mind) · Zhongwen Xu (DeepMind) · Manuel Kroiss (DeepMind) · Hado van Hasselt (DeepMind) · David Silver (Google DeepMind) · Satinder Singh (DeepMind) - Learning Near Optimal Policies with Low Inherent Bellman Error Andrea Zanette (Stanford University) · Alessandro Lazaric (Facebook AI Research) · Mykel Kochenderfer (Stanford University) · Emma Brunskill (Stanford University)
四、Batch RL
- GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Shangtong Zhang (University of Oxford) · Bo Liu (Auburn University) · Shimon Whiteson (University of Oxford) - An Optimistic Perspective on Offline Deep Reinforcement Learning
Rishabh Agarwal (Google Research, Brain Team) · Dale Schuurmans (Google / University of Alberta) · Mohammad Norouzi (Google Brain) - Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Alberto Maria Metelli (Politecnico di Milano) · Flavio Mazzolini (Politecnico di Milano) · Lorenzo Bisi (Politecnico di Milano) · Luca Sabbioni (Politecnico di Milano) · Marcello Restelli (Politecnico di Milano) - Reducing Sampling Error in Batch Temporal Difference Learning
Brahma Pavse (University of Texas at Austin) · Ishan Durugkar (University of Texas at Austin) · Josiah Hanna ( University of Edinburgh) · Peter Stone (University of Texas at Austin) - Batch Reinforcement Learning with Hyperparameter Gradients
Byung-Jun Lee (KAIST) · Jongmin Lee (KAIST) · Peter Vrancx (http://PROWLER.io) · Dongho Kim (http://Prowler.io) · Kee-Eung Kim (KAIST)
五、Imitation Learning
- Variational Imitation Learning with Diverse-quality Demonstrations
Voot Tangkaratt (RIKEN AIP) · Bo Han (HKBU / RIKEN) · Mohammad Emtiyaz Khan (RIKEN) · Masashi Sugiyama (RIKEN / The University of Tokyo) - Domain Adaptive Imitation Learning
Kuno Kim (Stanford University) · Yihong Gu (Tsinghua University) · Jiaming Song (Stanford) · Shengjia Zhao (Stanford University) · Stefano Ermon (Stanford University) - An Imitation Learning Approach for Cache Replacement
Evan Liu (Google) · Milad Hashemi (Google) · Kevin Swersky (Google Brain) · Parthasarathy Ranganathan (Google, USA) · Junwhan Ahn (Google) - Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Yufeng Zhang (Northwestern University) · Qi Cai (Northwestern University) · Zhuoran Yang (Princeton University) · Zhaoran Wang (Northwestern U) - Intrinsic Reward Driven Imitation Learning via Generative Model
Xingrui Yu (University of Technology Sydney) · Yueming LYU (University of Technology Sydney) · Ivor Tsang (University of Technology Sydney) - Provable Representation Learning for Imitation Learning via Bi-level Optimization
Sanjeev Arora ( Princeton University and Institute for Advanced Study) · Simon Du (Institute for Advanced Study) · Sham Kakade (University of Washington) · Yuping Luo (Princeton University) · Nikunj Umesh Saunshi (Princeton University) - Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel Brown (University of Texas at Austin) · Scott Niekum (University of Texas at Austin) · Russell Coleman (University of Texas at Austin) · Ravi Srinivasan (University of Texas at Austin)
六、Multi-Agent RL
- Kernel Methods for Cooperative Multi-Agent Learning with Delays
Abhimanyu Dubey (Massachusetts Institute of Technology) · Alex `Sandy' Pentland (MIT) - Robust Multi-Agent Decision-Making with Heavy-Tailed Payoffs
Abhimanyu Dubey (Massachusetts Institute of Technology) · Alex `Sandy' Pentland (MIT) - Multi-Agent Determinantal Q-Learning
Yaodong Yang (Huawei Technology R&D UK) · Ying Wen (UCL) · Jun Wang (UCL) · Liheng Chen (Shanghai Jiao Tong University) · Kun Shao (Huawei Noah's Ark Lab) · David Mguni (Noah's Ark Laboratory, Huawei) · Weinan Zhang (Shanghai Jiao Tong University) - Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang (Nanyang Technological University) · Xu He (Nanyang Technological University) · Runsheng Yu (Nanyang Technological University) · Wei Qiu (Nanyang Technological University) · Bo An (Nanyang Technological University) · Zinovi Rabinovich (Nanyang Technological University) - Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences
Somdeb Majumdar (Intel AI Lab) · Shauharda Khadka (Intel AI) · Santiago Miret (Intel AI Products Group) · Stephen Mcaleer (UC Irvine) · Kagan Tumer (Oregon State University US) - ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Tonghan Wang (Tsinghua University) · Heng Dong (Tsinghua) · Victor Lesser (UMASS) · Chongjie Zhang (Tsinghua University) - OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Alexander Vezhnevets (DeepMind) · Yuhuai Wu (University of Toronto) · Maria Eckstein (UC Berkeley) · Rémi Leblond (DeepMind) · Joel Z Leibo (DeepMind) - Multi-Agent Routing Value Iteration Network
Quinlan Sykora (Uber ATG) · Mengye Ren (Uber ATG / University of Toronto) · Raquel Urtasun (Uber ATG) - Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang (Tianjin University) · Jianye Hao (Tianjin University) · Guangyong Chen (Tencent) · Hongyao Tang (Tianjin University) · Yingfeng Chen (NetEase Fuxi AI Lab) · Yujing Hu (NetEase Fuxi AI Lab) · Changjie Fan (Netease) · Zhongyu Wei (Fudan University) - Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Tianyi Lin (UC Berkeley) · Zhengyuan Zhou (Stanford University) · Panayotis Mertikopoulos (CNRS) · Michael Jordan (UC Berkeley) - “Other-Play” for Zero-Shot Coordination
Hengyuan Hu (FAIR) · Alexander Peysakhovich (Facebook) · Adam Lerer (Facebook AI Research) · Jakob Foerster (Facebook AI Research) - Asynchronous Coagent Networks
James Kostas (University of Massachusetts Amherst) · Chris Nota (University of Massachusetts Amherst) · Philip Thomas (University of Massachusetts Amherst) - Extra-gradient with player sampling for faster convergence in n-player games
Samy Jelassi (Princeton University) · Carles Domingo-Enrich (NYU) · Damien Scieur (Samsung - SAIT AI Lab, Montreal) · Arthur Mensch (ENS) · Joan Bruna (New York University) - Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Yuxuan Xie (INSA de Lyon) · Jilles Dibangoye (INSA Lyon, INRIA) · Olivier Buffet (INRIA - LORIA)
七、Multi-Objective RL
- Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
Umer Siddique (Shanghai Jiao Tong University) · Paul Weng (Shanghai Jiao Tong University) · Matthieu Zimmer (UM-SJTU JI) - Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Jie Xu (Massachusetts Institute of Technology) · Yunsheng Tian (Massachusetts Institute of Technology) · Pingchuan Ma (MIT) · Daniela Rus (MIT CSAIL) · Shinjiro Sueda (Texas A&M University) · Wojciech Matusik (MIT) - A distributional view on multi objective policy optimization
Abbas Abdolmaleki (Google DeepMind) · Sandy Huang (DeepMind) · Leonard Hasenclever (DeepMind) · Michael Neunert (Google DeepMind) · Martina Zambelli (DeepMind) · Murilo Martins (DeepMind) · Francis Song (DeepMind) · Nicolas Heess (DeepMind) · Raia Hadsell (DeepMind) · Martin Riedmiller (DeepMind)
八、Policy Gradient
- From Importance Sampling to Doubly Robust Policy Gradient
Jiawei Huang (University of Illinois at Urbana-Champaign) · Nan Jiang (University of Illinois at Urbana-Champaign) - Statistically Efficient Off-Policy Policy Gradients
Nathan Kallus (Cornell University) · Masatoshi Uehara (Harvard University) - Momentum-Based Policy Gradient Methods
Feihu Huang (University of Pittsburgh) · Shangqian Gao (University of Pittsburgh) · Jian Pei (Simon Fraser University) · Heng Huang (University of Pittsburgh & JD Finance America Corporation) - On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei (Google / University of Alberta) · Chenjun Xiao (Google / University of Alberta) · Csaba Szepesvari (DeepMind/University of Alberta) · Dale Schuurmans (University of Alberta)
九、Off-Policy Evaluation
- Batch Stationary Distribution Estimation
Junfeng Wen (University of Alberta) · Bo Dai (Google Brain) · Lihong Li (Google Research) · Dale Schuurmans (University of Alberta) - Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara (Harvard University) · Jiawei Huang (University of Illinois at Urbana-Champaign) · Nan Jiang (University of Illinois at Urbana-Champaign) - Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Yao Liu (Stanford University) · Pierre-Luc Bacon (Stanford University) · Emma Brunskill (Stanford University) - Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
Nathan Kallus (Cornell University) · Masatoshi Uehara (Harvard University) - Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su (Cornell University) · Pavithra Srinath (Microsoft Research) · Akshay Krishnamurthy (Microsoft Research) - Doubly robust off-policy evaluation with shrinkage
Yi Su (Cornell University) · Maria Dimakopoulou (Stanford University) · Akshay Krishnamurthy (Microsoft Research) · Miroslav Dudik (Microsoft Research) - Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Yaqi Duan (Princeton University) · Zeyu Jia (Peking University) · Mengdi Wang (Princeton University) - Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics
Yihao Feng (The University of Texas at Austin) · Tongzheng Ren (UT Austin) · Ziyang Tang (University of Texas at Austin) · Qiang Liu (UT Austin) - Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman (Harvard University) · Joseph Futoma (Harvard University) · Yao Liu (Stanford University) · Sonali Parbhoo (Harvard University) · Leo Celi (MIT) · Emma Brunskill (Stanford University) · Finale Doshi-Velez (Harvard University)
十、Application
- Description Based Text Classification with Reinforcement Learning
Wei Wu (Shannon.AI) · Duo Chai (Shannon.AI) · Qinghong Han (Shannon.AI) · Fei Wu (Zhejiang University, China) · Jiwei Li (Shannon.AI) - Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor Simm (Cambridge University) · Robert Pinsler (University of Cambridge) · Jose Hernandez-Lobato (University of Cambridge) - Entropy Minimization In Emergent Languages
Evgeny Kharitonov (FAIR) · Rahma Chaabouni (Facebook/ENS/INRIA) · Diane Bouchacourt (Facebook AI) · Marco Baroni (Facebook Artificial Intelligence Research) - Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Tung-Che Liang (Duke University) · Zhanwei Zhong (Duke University) · Yaas Bigdeli (Duke Univsersity) · Tsung-Yi Ho (National Tsing Hua University) · Richard Fair (Duke University) · Krishnendu Chakrabarty (Duke University) - Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Rasheed El-Bouri (University of Oxford) · David Eyre (University of Oxford) · Peter Watkinson (Oxford University Hospitals NHS Foundation Trust) · Tingting Zhu (University of Oxford) · David Clifton (University of Oxford) - Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati (99andBeyond) · Boris Sattarov (99andBeyond) · Sufeng Niu (Linkedin) · Haoran Wei (University of Delaware) · Yashaswi Pathak (International Institute of Information Technology,Hyderabad) · Shengchao Liu (MILA-UdeM) · Shengchao Liu (Mila, Université de Montréal) · Simon Blackburn (Mila) · Karam Thomas (99andBeyond) · Connor Coley (MIT) · Jian Tang (HEC Montreal & MILA) · Sarath Chandar (Mila / École Polytechnique de Montréal) · Yoshua Bengio (Mila / U. Montreal)
十一、Other
- Generalization to New Actions in Reinforcement Learning
Ayush Jain (University of Southern California) · Andrew Szot (University of Southern California) · Joseph Lim (Univ. of Southern California) - Generalized Neural Policies for Relational MDPs
Sankalp Garg (Indian Institute of Technology Delhi) · Aniket Bajpai (Indian Institute of Technology, Delhi) · Mausam (IIT Delhi) - Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang (University of Michigan) · Aditya Modi (University of Michigan) · Michael Sjoding (University of Michigan) · Jenna Wiens (University of Michigan) - Learning the Valuations of a k-demand Agent
Hanrui Zhang (Duke University) · Vincent Conitzer (Duke) - Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Shangtong Zhang (University of Oxford) · Bo Liu (Auburn University) · Hengshuai Yao (Huawei Technologies) · Shimon Whiteson (University of Oxford) - Learning Human Objectives by Evaluating Hypothetical Behavior
Siddharth Reddy (University of California, Berkeley) · EECS Anca Dragan (EECS Department, University of California, Berkeley) · Sergey Levine (UC Berkeley) · Shane Legg (DeepMind) · Jan Leike (DeepMind) - Optimizing Data Usage via Differentiable Rewards
Xinyi Wang (Carnegie Mellon University) · Hieu Pham (Carnegie Mellon University) · Paul Michel (Carnegie Mellon University) · Antonios Anastasopoulos (Carnegie Mellon University) · Jaime Carbonell (Carnegie Mellon University) · Graham Neubig (Carnegie Mellon University) - Taylor Expansion Policy Optimization
Yunhao Tang (Columbia University) · Michal Valko (DeepMind) · Remi Munos (DeepMind) - Reinforcement Learning for Integer Programming: Learning to Cut
Yunhao Tang (Columbia University) · Shipra Agrawal (Columbia University) · Yuri Faenza (Columbia University) - Safe Reinforcement Learning in Constrained Markov Decision Processes
Akifumi Wachi (IBM Research AI) · Yanan Sui (Tsinghua University) - Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt (DeepMind) · Matteo Hessel (Deep Mind) · Karen Simonyan (DeepMind) - Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Amin Rakhsha (MPI-SWS) · Goran Radanovic (Max Planck Institute for Software Systems) · Rati Devidze (Max Planck Institute for Software Systems) · Jerry Zhu (University of Wisconsin-Madison) · Adish Singla (Max Planck Institute (MPI-SWS)) - Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi (London School of Economics and Political Science) · Runzhe Wan (North Carolina State University) · Rui Song () · Wenbin Lu () · Ling Leng (Amazon) - ConQUR: Mitigating Delusional Bias in Deep Q-Learning
DiJia Su (Princeton University) · Jayden Ooi (Google) · Tyler Lu (Google) · Dale Schuurmans (Google / University of Alberta) · Craig Boutilier (Google) - Self-Attentive Associative Memory
Hung Le (Deakin University) · Truyen Tran (Deakin University) · Svetha Venkatesh (Deakin University) - Striving for simplicity and performance in off-policy DRL: Output Normalization and Non-Uniform Sampling
Che Wang (New York University) · Yanqiu Wu (New York University) · Quan Vuong (University of California San Diego) · Keith Ross (New York University Shanghai) - Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Trevor Davis (University of Alberta) · Martin Schmid (DeepMind) · Michael Bowling (DeepMind) - Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Victor Campos (Barcelona Supercomputing Center) · Alexander Trott (Salesforce Research) · Caiming Xiong (Salesforce) · Richard Socher (Salesforce) · Xavier Giro-i-Nieto (Universitat Politecnica de Catalunya) · Jordi Torres (Barcelona Supercomputing Center) - Discount Factor as a Regularizer in Reinforcement Learning
Ron Amit (Technion – Israel Institute of Technology) · Kamil Ciosek (Microsoft) · Ron Meir (Technion Israeli Institute of Technology) - Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Lingxiao Wang (Northwestern University) · Zhuoran Yang (Princeton University) · Zhaoran Wang (Northwestern U) - Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian (University of Alberta) · Andrew Patterson (University of Alberta) · Shivam Garg (University of alberta) · Dhawal Gutpa (University of Alberta) · Adam White (University of Alberta) · Martha White (University of Alberta) - A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu (University of California, Los Angeles) · Quanquan Gu (University of California, Los Angeles) - Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Johannes Fischer (Karlsruhe Institute of Technology (KIT)) · Ömer Sahin Tas (Karlsruhe Institute of Technology (KIT)) - Learning Portable Representations for High-Level Planning
Steven James (University of the Witwatersrand) · Benjamin Rosman (University of the Witwatersrand / CSIR, South Africa) · George Konidaris (Brown) - The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
Ramin Hasani (TU Wien) · Mathias Lechner (IST Austria) · Alexander Amini (MIT) · Daniela Rus (MIT CSAIL) · Radu Grosu (TU Wien) - Reinforcement Learning with Differential Privacy
Giuseppe Vietri (University of Minnesota) · Borja de Balle Pigem (Amazon Research) · Steven Wu (University of Minnesota) · Akshay Krishnamurthy (Microsoft Research) - Growing Action Spaces
Gregory Farquhar (University of Oxford) · Laura Gustafson (Facebook AI Research) · Zeming Lin (Facebook AI Reseach) · Shimon Whiteson (Oxford University) · Nicolas Usunier (Facebook AI Research) · Gabriel Synnaeve (Facebook AI Research) - Responsive Safety in Reinforcement Learning
Adam Stooke (UC Berkeley) · Joshua Achiam (OpenAI) · Pieter Abbeel (UC Berkeley & Covariant) - Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto (Carnegie Mellon University) · Francis Song (DeepMind) · Jack Rae (DeepMind) · Razvan Pascanu (DeepMind) · Caglar Gulcehre (DeepMind) · Siddhant Jayakumar (DeepMind) · Max Jaderberg (DeepMind) · Raphael Lopez Kaufman (Deepmind) · Aidan Clark (DeepMind) · Seb Noury (DeepMind) · Matthew Botvinick (DeepMind) · Nicolas Heess (DeepMind) · Raia Hadsell (DeepMind) - Learning to Score Behaviors for Guided Policy Optimization
Aldo Pacchiano (UC Berkeley) · Jack Parker-Holder (University of Oxford) · Yunhao Tang (Columbia University) · Krzysztof Choromanski (Google) · Anna Choromanska (NYU Tandon School of Engineering) · Michael Jordan (UC Berkeley) - Efficient Policy Learning from Surrogate-Loss Classification Reductions
Andrew Bennett (Cornell University) · Nathan Kallus (Cornell University) - Constrained Markov Decision Processes via Backward Value Functions
Harsh Satija (McGill University) · Philip Amortila (McGill University) · Joelle Pineau (McGill University / Facebook) - Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan (California Institute of Technology) · Albert Tseng (Caltech) · Yisong Yue (Caltech) · Adith Swaminathan (Microsoft Research) · Matthew Hausknecht (Microsoft Research) - Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar (Facebook AI Research) · Abhinav Gupta (Carnegie Mellon University) - Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe (OpenAI) · Chris Hesse (OpenAI) · Jacob Hilton (OpenAI) · John Schulman (OpenAI) - What can I do here? A Theory of Affordances in Reinforcement Learning
Khimya Khetarpal (McGill University, Mila Montreal) · Zafarali Ahmed (DeepMind) · Gheorghe Comanici (DeepMind) · David Abel (Brown University) · Doina Precup (DeepMind) - Data Valuation using Reinforcement Learning
Jinsung Yoon (Google) · Sercan O. Arik (Google) · Tomas Pfister (Google) - Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
Junzhe Zhang (Columbia University) - Lookahead-Bounded Q-learning
Ibrahim El Shar (University of Pittsburgh) · Daniel Jiang (University of Pittsburgh) - Evaluating the Performance of Reinforcement Learning Algorithms
Scott Jordan (University of Massachusetts Amherst) · Yash Chandak (University of Massachusetts Amherst) · Daniel Cohen (University of Massachusetts Amherst) · Mengxue Zhang (umass Amherst ) · Philip Thomas (University of Massachusetts Amherst) - Provable Self-Play Algorithms for Competitive Reinforcement Learning
Yu Bai (Salesforce Research) · Chi Jin (Princeton University) - Optimizing for the Future in Non-Stationary MDPs
Yash Chandak (University of Massachusetts Amherst) · Georgios Theocharous (Adobe Research) · Shiv Shankar (University of Massachusetts) · Martha White (University of Alberta) · Sridhar Mahadevan (Adobe Research) · Philip Thomas (University of Massachusetts Amherst) - Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Aleksei Petrenko (University of Southern California) · Zhehui Huang (University of Southern California) · Tushar Kumar (University of Southern California) · Gaurav Sukhatme (University of Southern California) · Vladlen Koltun (Intel Labs) - When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Feng Zhu (Peking University) · Zeyu Zheng (University of California, Berkeley) - Structured Policy Iteration for Linear Quadratic Regulator
Youngsuk Park (Stanford University) · Ryan Rossi (Adobe Research) · Zheng Wen (DeepMind) · Gang Wu (Adobe Research) · Handong Zhao (Adobe Research) - Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill (DeepMind) · Florent Altché (DeepMind) · Yunhao Tang (Columbia University) · Thomas Hubert (DeepMind) · Michal Valko (DeepMind) · Ioannis Antonoglou (Deepmind) · Remi Munos (DeepMind) - On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong (Tsinghua University) · Yuping Luo (Princeton University) · Tianhe Yu (Stanford University) · Chelsea Finn (Stanford) · Tengyu Ma (Stanford) - Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota (Mitsubishi Electric Corporation) · Tomoaki Oiki (Mitsubishi Electric) · Devesh Jha (Mitsubishi Electric Research Labs) · Toshisada Mariyama (Mitsubishi Electric) · Daniel Nikovski (Mitsubishi Electric Research Labs) - Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
Tom Jurgenson (Technion) · Or Avner (Technion) · Edward Groshev (Osaro, Inc.) · Aviv Tamar (Technion) - Agent57: Outperforming the Atari Human Benchmark
Adrià Puigdomenech Badia (Deepmind) · Bilal Piot (DeepMind) · Steven Kapturowski (Deepmind) · Pablo Sprechmann (Google DeepMind) · Oleksandr Vitvitskyi (DeepMind) · Zhaohan Guo (DeepMind) · Charles Blundell (DeepMind) - Stochastically Dominant Distributional Reinforcement Learning
John Martin (Stevens Institute of Technology) · Michal Lyskawinski (Stevens Institute of Technology) · Xiaohu Li (Stevens Institute of Technology) · Brendan Englot (Stevens Institute of Technology) - Option Discovery in the Absence of Rewards with Manifold Analysis
Amitay Bar (Technion - Israel Institute of Technology) · Ronen Talmon (Technion - Israel Institute Of Technology) · Ron Meir (Technion Israeli Institute of Technology) - Gradient-free Online Learning in Continuous Games with Delayed Rewards
Amélie Héliou (Criteo) · Panayotis Mertikopoulos (CNRS) · Zhengyuan Zhou (Stanford University) - Fast Adaptation to New Environments via Policy-Dynamics Value Functions
Roberta Raileanu (NYU) · Max Goldstein (NYU) · Arthur Szlam (Facebook) · Facebook Rob Fergus (Facebook AI Research, NYU) - Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Zhaohan Guo (DeepMind) · Bernardo Avila Pires (DeepMind) · Mohammad Gheshlaghi Azar (Deepmind) · Bilal Piot (DeepMind) · Florent Altché (DeepMind) · Jean-Bastien Grill (DeepMind) · Remi Munos (DeepMind) - Deep Reinforcement Learning with Smooth Policy
Qianli Shen (Peking University) · Yan Li (Georgia Tech) · Haoming Jiang (Georgia Tech) · Zhaoran Wang (Northwestern) · Tuo Zhao (Gatech) - Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
Subho Banerjee (University of Illinois at Urbana-Champaign) · Saurabh Jha (UIUC) · Zbigniew Kalbarczyk (University of Illinois at Urbana-Champaign) · Ravishankar Iyer (University of Illinois at Urbana-Champaign) - Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr Pong (UC Berkeley) · Murtaza Dalal (UC Berkeley) · Steven Lin (UC Berkeley) · Ashvin Nair (UC Berkeley) · Shikhar Bahl (UC Berkeley/Carnegie Mellon University) · Sergey Levine (UC Berkeley) - Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei (University of Southern California) · Mehdi Jafarnia (University of Southern California) · Haipeng Luo (University of Southern California) · Hiteshi Sharma (University of Southern California) · Rahul Jain (USC) - Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra (Microsoft) · Mikael Henaff (Microsoft) · Akshay Krishnamurthy (Microsoft Research) · John Langford (Microsoft Research) - Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang (Uber AI) · Joel Lehman () · Aditya Rawal (Uber AI Labs) · Jiale Zhi (Uber AI) · Yulun Li (Uber AI) · Jeffrey Clune (Open AI) · Kenneth Stanley (Uber AI and University of Central Florida) - Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang (UW-Madison) · Yuzhe Ma (Univ. of Wisconsin-Madison) · Adish Singla (Max Planck Institute (MPI-SWS)) · Jerry Zhu (University of Wisconsin-Madison) - Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar (MIT) · Fredrik Johansson (Chalmers University of Technology) · John Guttag (MIT) · David Sontag (Massachusetts Institute of Technology) - Sequential Transfer in Reinforcement Learning with a Generative Model
Andrea Tirinzoni (Politecnico di Milano) · Riccardo Poiani (Politecnico di Milano) · Marcello Restelli (Politecnico di Milano) - Interference and Generalization in Temporal Difference Learning
Emmanuel Bengio (McGill University) · Joelle Pineau (McGill University / Facebook) · Doina Precup (McGill University / DeepMind) - CoMic: Co-Training and Mimicry for Reusable Skills
Leonard Hasenclever (DeepMind) · Fabio Pardo (Imperial College London) · Raia Hadsell (DeepMind) · Nicolas Heess (DeepMind) · Josh Merel (DeepMind) - Stochastic Regret Minimization in Extensive-Form Games
Gabriele Farina (Carnegie Mellon University) · Christian Kroer (Columbia University) · Tuomas Sandholm (Carnegie Mellon University) - Logarithmic Regret for Online Control with Adversarial Noise
Dylan Foster (MIT) · Max Simchowitz (UC Berkeley) - Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Jesse Zhang (UC Berkeley) · Brian Cheung (UC Berkeley) · Chelsea Finn (Stanford) · Sergey Levine (UC Berkeley) · Dinesh Jayaraman (University of Pennsylvania) - Representations for Stable Off-Policy Reinforcement Learning
Dibya Ghosh (Google) · Marc Bellemare (Google Brain) - Multi-Step Greedy Reinforcement Learning Algorithms
Manan Tomar (Indian Institute of Technology, Madras) · Yonathan Efroni (Technion) · Mohammad Ghavamzadeh (Facebook AI Research) - Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-Shyang Wang (Argo AI) · Tsui-Wei Weng (MIT) · Luca Daniel (MIT) - Estimating Q(s,s') with Deterministic Dynamics Gradients
Ashley Edwards (Uber AI) · Himanshu Sahni (Georgia Institute of Technology) · Rosanne Liu (Deep Collective) · Jane Hung (Uber) · Ankit Jain (Uber AI Labs) · Rui Wang (Uber AI) · Adrien Ecoffet (OpenAI) · Thomas Miconi (Uber AI Labs) · Charles Isbell (Georgia Institute of Technology) · Jason Yosinski (Uber Labs) - CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning
Michael Laskin (UC Berkeley) · Pieter Abbeel (UC Berkeley & Covariant) · Aravind Srinivas (UC Berkeley) - Inferring DQN structure for high-dimensional continuous control
Andrey Sakryukin (National University of Singapore) · Chedy Raissi (INRIA) · Mohan Kankanhalli (National University of Singapore,) - R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Zhongxiang Dai (National University of Singapore) · Yizhou Chen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Patrick Jaillet (MIT) · Teck-Hua Ho (National University of Singapore) - Revisiting Fundamentals of Experience Replay
William Fedus (University of Montreal/Google Brain) · Prajit Ramachandran (Google) · Rishabh Agarwal (Google Research, Brain Team) · Yoshua Bengio (Mila / U. Montreal) · Hugo Larochelle (Google Brain) · Mark Rowland (DeepMind) · Will Dabney (DeepMind) - Predictive Coding for Locally-Linear Control
Rui Shu (Stanford University) · Tung Nguyen (VinAI Research) · Yinlam Chow (Google) · Tuan Pham (VinAI) · Khoat Than (VinAI & HUST) · Mohammad Ghavamzadeh (Facebook) · Stefano Ermon (Stanford University) · Hung Bui (VinAI Research) - Efficiently Solving MDPs with Stochastic Mirror Descent
Yujia Jin (Stanford University) · Aaron Sidford (Stanford) - Hierarchically Decoupled Morphological Transfer
Donald Hejna (UC Berkeley) · Lerrel Pinto (NYU/Berkeley) · Pieter Abbeel (UC Berkeley) - Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
Arsenii Kuznetsov (Samsung) · Pavel Shvechikov (Samsung Artificial Intelligence Center ) · Alexander Grishin (Higher School of Economics) · Dmitry Vetrov (Higher School of Economics, Samsung AI Center Moscow) - Invariant Causal Prediction for Block MDPs
Clare Lyle (University of Oxford) · Amy Zhang (McGill University) · Angelos Filos (University of Oxford) · Shagun Sodhani (Facebook AI Research) · Marta Kwiatkowska (Oxford University) · Yarin Gal (University of Oxford) · Doina Precup (McGill University / DeepMind) · Joelle Pineau (McGill University / Facebook) - Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
Mahsa Ghasemi (The University of Texas at Austin) · Erdem Bulgur (University of Texas at Austin) · Ufuk Topcu (University of Texas at Austin)
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编辑于 2020-06-12 22:30