【最新重磅整理】82篇AAAI2021强化学习领域论文接收列表
深度强化学习实验室
官网：http://www.neurondance.com/
论坛：http://deeprl.neurondance.com/
作者：深度强化学习实验室&AMiner
编辑：DeepRL
416: Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation
Bo Pang, Zhong-‐Ping Jiang
676: Scalable First-‐Order Methods for Robust MDPs
Julien Grand Clement, Christian Kroer
710: Maintenance of Social Commitments in Multiagent Systems
Pankaj Telang, Munindar Singh, Neil Yorke-‐Smith
1137: Self-‐Supervised Attention-‐Aware Reinforcement Learning
Haiping Wu, Khimya Khetarpal, Doina Precup
1169: Hierarchical Reinforcement Learning for Integrated Recommendation
Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, Leyu Lin
2088: Combining Reinforcement Learning with Lin-‐Kernighan-‐Helsgaun Algorithm for the Traveling Salesman Problem
Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chumin Li
2136: Learning to Reweight Imaginary Transitions for Model-‐Based Reinforcement Learning
Wenzhen Huang, Qiyue Yin, Junge Zhang, KAIQI HUANG
2294: Exploration-‐Exploitation in Multi-‐Agent Learning: Catastrophe Theory Meets Game Theory
Stefanos Leonardos, Georgios Piliouras
2431: Advice-‐Guided Reinforcement Learning in a Non-‐Markovian Environment
Daniel Neider, Jean-‐Raphaël Gaglione, Ivan Gavran, Ufuk Topcu, Bo Wu, Zhe Xu
2441: Content Masked Loss: Human-‐Like Brush Stroke Planning in a Reinforcement Learning Painting Agent
Peter Schaldenbrand, Jean Oh
2453: Metrics and Continuity in Reinforcement Learning
Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro
2666: Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning
Andrea Micheli, Alessandro Valentini
2971: Lipschitz Lifelong Reinforcement Learning
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
3011: Exact Reduction of Huge Action Spaces in General Reinforcement Learning
Sultan Javed Majeed, Marcus Hutter
3094: Visual Tracking via Hierarchical Deep Reinforcement Learning
Dawei Zhang, Zhonglong Zheng, Riheng Jia, Minglu Li
3193: Adaptive Prior-‐Dependent Correction Enhanced Reinforcement Learning for Natural Language Generation
Wei Cheng, Ziyan Luo, Qiyue Yin
3279: A Hybrid Stochastic Gradient Hamiltonian Monte Carlo Method
Chao Zhang, Zhijian Li, Zebang Shen, Jiahao Xie, Hui Qian
3412: Sequential Generative Exploration Model for Partially Observable Reinforcement Learning
Haiyan Yin, Jianda Chen, Sinno Pan, Sebastian Tschiatschek
3679: Learning Task-‐Distribution Reward Shaping with Meta-‐Learning
Haosheng Zou, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu
3727: Visual Comfort Aware-‐Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images
Hak Gu Kim, Minho Park, Sangmin Lee, Seongyeop Kim, Yong Man Ro
3812: Scheduling of Time-‐Varying Workloads Using Reinforcement Learning
Shanka Subhra Mondal, Nikhil Sheoran, Subrata Mitra
4386: DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Jiliang Tang , Hui Liu
4719: Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-‐Player Games
David Milec, Jakub Cerny, Viliam Lisy, Bo An
4999: Bayesian Optimized Monte Carlo Planning
John Mern, Anil Yildiz, Zachary Sunberg, Tapan Mukerji, Mykel Kochenderfer
5008: Towards Effective Context for Meta-‐Reinforcement Learning: An Approach Based on Contrastive Learning
Haotian Fu, Hongyao Tang, Jianye Hao, Chen Chen, Xidong Feng, Dong Li, Wulong Liu
5012: Improved POMDP Tree Search Planning with Prioritized Action Branching
John Mern, Anil Yildiz, Lawrence Bush, Tapan Mukerji, Mykel Kochenderfer
5046: Anytime Heuristic and Monte Carlo Methods for Large-‐Scale Simultaneous Coalition Structure Generation and Assignment
Fredrik Präntare, Fredrik Heintz, Herman Appelgren
5101: Reinforcement Learning with Trajectory Feedback
Yonathan Efroni, Nadav Merlis, Shie Mannor
5167: Encoding Human Domain Knowledge to Warm Start Reinforcement Learning
Andrew Silva, Matthew Gombolay
5284: GLIB: Efficient Exploration for Relational Model-‐Based Reinforcement Learning via Goal-Literal Babbling
Rohan Chitnis, Tom Silver, Joshua Tenenbaum, Leslie Kaelbling, Tomas Lozano-‐Perez
5303: Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size
Christoph Hertrich, Martin Skutella
5320: WCSAC: Worst-‐Case Soft Actor Critic for Safety-‐Constrained Reinforcement Learning
Qisong Yang, Thiago D. Simão, Simon H Tindemans, Matthijs T. J. Spaan
5334: Queue-‐Learning: A Reinforcement Learning Approach for Providing Quality of Service
Majid Raeis, Ali Tizghadam, Alberto Leon-‐Garcia
5546: Improving Sample Efficiency in Model-‐Free Reinforcement Learning from Images
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus
5657: A Sample-‐Efficient Algorithm for Episodic Finite-‐Horizon MDP with Constraints
Krishna C Kalagarla, Rahul Jain, Pierluigi Nuzzo
5712: Resilient Multi-‐Agent Reinforcement Learning with Adversarial Value Decomposition
Thomy Phan, Lenz Belzner, Thomas Gabor, Andreas Sedlmeier, Fabian Ritz, Claudia Linnhoff-Popien
5906: Domain Adaptation in Reinforcement Learning via Latent Unified State Representation
Jinwei Xing, Takashi Nagata, Kexin Chen, Xinyun Zou, Emre Neftci, Jeffrey Prof. Krichmar
5930: Uncertainty-‐Aware Policy Optimization: A Robust, Adaptive Trust Region Approach
James Queeney, Ioannis Paschalidis, Christos G. Cassandras
5971: Deep Recurrent Belief Propagation Network for POMDPs
Yuhui Wang, Xiaoyang Tan
6031: Inverse Reinforcement Learning from Like-‐Minded Teachers
Ritesh Noothigattu, Tom Yan, Ariel D Procaccia
6049: FontRL: Chinese Font Synthesis via Deep Reinforcement Learning
Yitian Liu, Zhouhui Lian
6070: Coordination between Individual Agents in Multi-‐Agent Reinforcement Learning
Yang Zhang, Qingyu Yang, Dou An, Chengwei Zhang
6211: Constrained Risk-‐Averse Markov Decision Processes
Mohamadreza Ahmadi, Ugo Rosolia, Michel Ingham, Richard M Murray, Aaron Ames
6310: A Deep Reinforcement Learning Approach to First-‐Order Logic Theorem Proving
Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue
6343: The Maximin Support Method: An Extension of the D’Hondt Method to Approval-‐Based Multiwinner Elections
Luis Sanchez-‐Fernandez, Norberto Fernández García, Jesús Fisteus, Markus Brill
6428: Reinforcement Learning Based Multi-‐Agent Resilient Control: From Deep Neural Networks to an Adaptive Law
Jian Hou, Fangyuan Wang, Lili Wang, Zhiyong Chen
6610: Learning Game-‐Theoretic Models of Multiagent Trajectories Using Implicit Layers
Philipp Geiger, Christoph-‐Nikolas Straehle
6977: DeepTrader: A Deep Reinforcement Learning Approach for Risk-‐Return Balanced Portfolio Management with Market Conditions Embedding
Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, Lei Xu
7018: Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
Kai Wang, Zhene Zou, Qilin Deng, Jianrong Tao, Runze Wu, Changjie Fan, Liang Chen, Peng Cui
7394: Learning Model-‐Based Privacy Protection under Budget Constraints
Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
7572: Towards Fully Automated Manga Translation
Ryota Hinami, Shonosuke Ishiwatari, Kazuhiko Yasuda, Yusuke Matsui
7657: The Value-‐Improvement Path: Towards Better Representations for Reinforcement Learning
Will Dabney, Andre Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
7812: Text-‐Based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
7911: DSLR : Dynamic to Static Lidar Scan Reconstruction Using Adversarially Trained Auto Encoder
Prashant Kumar, Sabyasachi Sahoo, Vanshil Shah, Vineetha Kondameedi, Abhinav Jain, Akshaj Verma, Chiranjib Bhattacharyya, Vinay Vishwanath
7936: Dynamic Automaton-‐Guided Reward Shaping for Monte Carlo Tree Search
Alvaro Velasquez, Brett Bissey, Lior Barak, Andre Beckus, Ismail Alkhouri, Daniel Melcer, George Atia
7952: Sample Efficient Reinforcement Learning with REINFORCE
Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen Boyd
8029: Reinforcement Learning of Sequential Price Mechanisms
Gianluca Brero, Alon Eden, Matthias Gerstgrasser, David Parkes, Duncan Rheingans-‐Yoo
8042: Robust Finite-‐State Controllers for Uncertain POMDPs
Murat Cubuktepe, Nils Jansen, Sebastian Junges, Ahmadreza Marandi, Marnix Suilen, Ufuk Topcu
8168: TAC: Towered Actor Critic for Handling Multiple Action Types in Reinforcement Learning for Drug Discovery
Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, . Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, Sarath Chandar
8181: Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs
Aria HasanzadeZonuzy, Archana Bura, Dileep Kalathil, Srinivas Shakkottai
8186: Solving Common-‐Payoff Games with Approximate Policy Iteration
Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot
8323: DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate , Tom Melham, Daniel Kroening
8398: Inverse Reinforcement Learning with Explicit Policy Estimates
Navyata Sanghvi, Shinnosuke Usami, Mohit Sharma, Joachim Groeger, Kris Kitani
8545: Mean-‐Variance Policy Iteration for Risk-‐Averse Reinforcement Learning
Shangtong Zhang, Bo Liu, Shimon Whiteson
8556: Iterative Bounding MDPs: Learning Interpretable Policies via Non-‐Interpretable Methods
Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso
8619: Temporal-‐Logic-‐Based Reward Shaping for Continuing Reinforcement Learning Tasks
Yuqian Jiang, Sudarshanan Bharadwaj, Bo Wu, Rishi Shah, Ufuk Topcu, Peter Stone
8771: Online 3D Bin Packing with Constrained Deep Reinforcement Learning
Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, Kai Xu
9385: A General Offline Reinforcement Learning Framework for Interactive Recommendation
Teng Xiao, Donglin Wang
9457: Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes
Marc Rigter, Bruno Lacerda, Nick Hawes
9459: Planning from Pixels in Atari with Learned Symbolic Representations
Andrea Dittadi, Frederik K Drachmann, Thomas Bolander
9813: Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
Quentin Cappart, Thierry Moisan, Louis-‐Martin Rousseau, Isabeau Prémont-‐Schwarz, Andre Cire
9862: Distributional Reinforcement Learning via Moment Matching
Thanh Tang Nguyen, Sunil Gupta, Svetha Venkatesh
9869: Non-‐Asymptotic Convergence of Adam-‐Type Reinforcement Learning Algorithms under Markovian Sampling
Huaqing Xiong, Tengyu Xu, Yingbin Liang, Wei Zhang
9983: Data-‐Driven Competitive Algorithms for Online Knapsack and Set Cover
Ali Zeynali, Bo Sun, Mohammad Hajiesmaili, Adam Wierman
10000: Inverse Reinforcement Learning with Natural Language Goals
Li Zhou, Kevin Small
10014: Decentralized Policy Gradient Descent Ascent for Safe Multi-‐Agent Reinforcement Learning
Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Basar, Lior Horesh
10033: Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion
Josh Roy, George Konidaris
10098: Policy Optimization as Online Learning with Mediator Feedback
Alberto Maria Metelli, Matteo Papini, Pierluca D'Oro, Marcello Restelli
10284: Model-‐Free Online Learning in Unknown Sequential Decision Making Problems and Games
Gabriele Farina
10346: Deep Bayesian Quadrature Policy Optimization
Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Animashree Anandkumar, Yisong Yue
7256: K-‐N-‐MOMDPs: Towards Interpretable Solutions for Adaptive Management
Jonathan Ferrer Mestres, Thomas Dietterich, Olivier Buffet, Iadine Chades
本文同步发布于《深度强化学习实验室》
http://deeprl.neurondance.com/d/191-84aaai2021
（或点击公众底端“阅读原文”）
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：深度强化学习理论、模型及编码调参技巧
完
第101篇：OpenAI科学家提出全新强化学习算法
第100篇：Alchemy: 元强化学习(meta-RL)基准环境
第99篇：NeoRL:接近真实世界的离线强化学习基准
第98篇：全面总结(值函数与优势函数)的估计方法
第97篇：MuZero算法过程详细解读
第96篇: 值分布强化学习（Distributional RL）总结
第95篇：如何提高"强化学习算法模型"的泛化能力?
第94篇：多智能体强化学习《星际争霸II》研究
第93篇：MuZero在Atari基准上取得了新SOTA效果
第92篇：谷歌AI掌门人Jeff Dean获冯诺依曼奖
第91篇：详解用TD3算法通关BipedalWalker环境
第90篇：Top-K Off-Policy RL论文复现
第89篇：腾讯开源分布式多智能TLeague框架
第88篇：分层强化学习(HRL)全面总结
第87篇：165篇CoRL2020 accept论文汇总
第86篇：287篇ICLR2021深度强化学习论文汇总
第85篇：279页总结"基于模型的强化学习方法"
第84篇：阿里强化学习领域研究助理/实习生招聘
第83篇：180篇NIPS2020顶会强化学习论文
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
第80篇：强化学习《奖励函数设计》详细解读
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
