AAAI 的英文全称是 Association for the Advance of Artificial Intelligence——美国人工智能协会。该协会是人工智能领域的主要学术组织之一，其主办的年会也是人工智能领域的国际顶级会议。在中国计算机学会的国际学术会议排名以及清华大学新发布的计算机科学推荐学术会议和期刊列表中，AAAI 均被列为人工智能领域的 A 类顶级会议。
AAAI 2021论文接收列表如下： https://aaai.org/Conferences/AAAI-21/wp-content/uploads/2020/12/AAAI-21Accepted-Paper-List.Main.Technical.Track_.pdf
其中有82篇强化学习领域论文。 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
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