allenpandas/2022-reinforcement-learning-conferences-papers

The proceedings of top conference in 2022 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.

2022-Reinforcement-Learning-Conferences-Papers

The proceedings of top conference in 2022 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.

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Todo

• Related repository
• AAAI'2022
• AAMAS'2022
• ICLR'2022
• ICML'2022
• ICRA'2022
• IJCAI'2022
• NeurIPS'2022

Contributing

Markdown format：

- **Paper Name**.
  [[pdf](link)]
  [[code](link)]
  - Author 1, Author 2, and Author 3. *conference, year*.

Please help to contribute this list by contacting me or add pull request.

For any questions, feel free to contact me 📮.

Table of Contents

• AAAI Conference on Artificial Intelligence (AAAI'2022)
• International Conference on Autonomous Agents and Multiagent Systems (AAMAS'2022)
• International Conference on Learning Representations (ICLR'2022)
• International Conference on Machine Learning (ICML'2022)
• International Conference on Robotics and Automation (ICRA'2022)
• International Joint Conference on Artificial Intelligence (IJCAI'2022)
• Annual Conference on Neural Information Processing Systems (NeurIPS'2022)

AAAI Conference on Artificial Intelligence

• Backprop-Free Reinforcement Learning with Active Neural Generative Coding. [pdf]
  • Alexander G. Ororbia II, Ankur Arjun Mali. AAAI 2022.
• Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning. [pdf]
  • Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Bo Xu. AAAI 2022.
• CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving. [pdf]
  • Yinuo Zhao, Kun Wu, Zhiyuan Xu, Zhengping Che, Qi Lu, Jian Tang, Chi Harold Liu. AAAI 2022.
• Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach. [pdf]
  • Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, Vaneet Aggarwal. AAAI 2022.
• OAM: An Option-Action Reinforcement Learning Framework for Universal Multi-Intersection Control. [pdf]
  • Enming Liang, Zicheng Su, Chilin Fang, Renxin Zhong. AAAI 2022.
• EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles. [pdf]
  • Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty. AAAI 2022.
• DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning. [pdf]
  • Xianyuan Zhan, Haoran Xu, Yue Zhang, Xiangyu Zhu, Honglei Yin, Yu Zheng. AAAI 2022.
• AlphaHoldem: High-Performance Artificial Intelligence for Heads-Up No-Limit Poker via End-to-End Reinforcement Learning. [pdf]
  • Enmin Zhao, Renye Yan, Jinqiu Li, Kai Li, Junliang Xing. AAAI 2022.
• Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning. [pdf]
  • Yecheng Jason Ma, Andrew Shen, Osbert Bastani, Dinesh Jayaraman. AAAI 2022.
• Robust Adversarial Reinforcement Learning with Dissipation Inequation Constraint. [pdf]
  • Peng Zhai, Jie Luo, Zhiyan Dong, Lihua Zhang, Shunli Wang, Dingkang Yang. AAAI 2022.
• Enforcement Heuristics for Argumentation with Deep Reinforcement Learning. [pdf]
  • Dennis Craandijk, Floris Bex. AAAI 2022.
• Programmatic Modeling and Generation of Real-Time Strategic Soccer Environments for Reinforcement Learning. [pdf]
  • Abdus Salam Azad, Edward Kim, Qiancheng Wu, Kimin Lee, Ion Stoica, Pieter Abbeel, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia. AAAI 2022.
• Learning by Competition of Self-Interested Reinforcement Learning Agents. [pdf]
  • Stephen Chung. AAAI 2022.
• Reinforcement Learning with Stochastic Reward Machines. [pdf]
  • Jan Corazza, Ivan Gavran, Daniel Neider. AAAI 2022.
• Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting. [pdf]
  • Yuwei Fu, Di Wu, Benoit Boulet. AAAI 2022.
• Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods. [pdf]
  • Xin Guo, Anran Hu, Junzi Zhang. AAAI 2022.
• Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks. [pdf]
  • Yijie Guo, Qiucheng Wu, Honglak Lee. AAAI 2022.
• Wasserstein Unsupervised Reinforcement Learning. [pdf]
  • Shuncheng He, Yuhang Jiang, Hongchang Zhang, Jianzhun Shao, Xiangyang Ji. AAAI 2022.
• Reinforcement Learning of Causal Variables Using Mediation Analysis. [pdf]
  • Tue Herlau, Rasmus Larsen. AAAI 2022.
• Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes. [pdf]
  • Guillermo Infante, Anders Jonsson, Vicenç Gómez. AAAI 2022.
• Creativity of AI: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning. [pdf]
  • Mu Jin, Zhihao Ma, Kebing Jin, Hankz Hankui Zhuo, Chen Chen, Chao Yu. AAAI 2022.
• Same State, Different Task: Continual Reinforcement Learning without Interference. [pdf]
  • Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, Stephen J. Roberts. AAAI 2022.
• Introducing Symmetries to Black Box Meta Reinforcement Learning. [pdf]
  • Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen. AAAI 2022.
• Deep Reinforcement Learning Policies Learn Shared Adversarial Features across MDPs. [pdf]
  • Ezgi Korkmaz. AAAI 2022.
• Conjugated Discrete Distributions for Distributional Reinforcement Learning. [pdf]
  • Björn Lindenberg, Jonas Nordqvist, Karl-Olof Lindahl. AAAI 2022.
• Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning. [pdf]
  • Jinxin Liu, Donglin Wang, Qiangxing Tian, Zhengyu Chen. AAAI 2022.
• Fast and Data Efficient Reinforcement Learning from Pixels via Non-parametric Value Approximation. [pdf]
  • Alexander Long, Alan Blair, Herke van Hoof. AAAI 2022.
• Recursive Reasoning Graph for Multi-Agent Reinforcement Learning. [pdf]
  • Xiaobai Ma, David Isele, Jayesh K. Gupta, Kikuo Fujimura, Mykel J. Kochenderfer. AAAI 2022.
• Exploring Safer Behaviors for Deep Reinforcement Learning. [pdf]
  • Enrico Marchesini, Davide Corsi, Alessandro Farinelli. AAAI 2022.
• Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning. [pdf]
  • Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill. AAAI 2022.
• Unsupervised Reinforcement Learning in Multiple Environments. [pdf]
  • Mirco Mutti, Mattia Mancassola, Marcello Restelli. AAAI 2022.
• Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. [pdf]
  • Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon. AAAI 2022.
• Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning. [pdf]
  • Giseung Park, Sungho Choi, Youngchul Sung. AAAI 2022.
• Offline Reinforcement Learning as Anti-exploration. [pdf]
  • Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist. AAAI 2022.
• Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability. [pdf]
  • Aviv Tamar, Daniel Soudry, Ev Zisselman. AAAI 2022.
• Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic. [pdf]
  • Zhihai Wang, Jie Wang, Qi Zhou, Bin Li, Houqiang Li. AAAI 2022.
• Controlling Underestimation Bias in Reinforcement Learning via Quasi-median Operation. [pdf]
  • Wei Wei, Yujia Zhang, Jiye Liang, Lin Li, Yuze Li. AAAI 2022.
• Structure Learning-Based Task Decomposition for Reinforcement Learning in Non-stationary Environments. [pdf]
  • Honguk Woo, Gwangpyo Yoo, Minjong Yoo. AAAI 2022.
• Generalizing Reinforcement Learning through Fusing Self-Supervised Learning into Intrinsic Motivation. [pdf]
  • Keyu Wu, Min Wu, Zhenghua Chen, Yuecong Xu, Xiaoli Li. AAAI 2022.
• Reinforcement Learning Augmented Asymptotically Optimal Index Policy for Finite-Horizon Restless Bandits. [pdf]
  • Guojun Xiong, Jian Li, Rahul Singh. AAAI 2022.
• Constraints Penalized Q-learning for Safe Offline Reinforcement Learning. [pdf]
  • Haoran Xu, Xianyuan Zhan, Xiangyu Zhu. AAAI 2022.
• Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning. [pdf]
  • Chen Yanai, Adir Solomon, Gilad Katz, Bracha Shapira, Lior Rokach. AAAI 2022.
• Natural Black-Box Adversarial Examples against Deep Reinforcement Learning. [pdf]
  • Mengran Yu, Shiliang Sun. AAAI 2022.
• SimSR: Simple Distance-Based State Representations for Deep Reinforcement Learning. [pdf]
  • Hongyu Zang, Xin Li, Mingzhong Wang. AAAI 2022.
• State Deviation Correction for Offline Reinforcement Learning. [pdf]
  • Hongchang Zhang, Jianzhun Shao, Yuhang Jiang, Shuncheng He, Guanwen Zhang, Xiangyang Ji. AAAI 2022.
• Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic. [pdf]
  • Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel. AAAI 2022.
• A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. [pdf]
  • Sai Qian Zhang, Jieyu Lin, Qi Zhang. AAAI 2022.
• Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning. [pdf]
  • Yuheng Zhang, Hanghang Tong, Yinglong Xia, Yan Zhu, Yuejie Chi, Lei Ying. AAAI 2022.
• Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms. [pdf]
  • Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov, Lillian J. Ratliff. AAAI 2022.
• Invariant Action Effect Model for Reinforcement Learning. [pdf]
  • Zheng-Mao Zhu, Shengyi Jiang, Yu-Ren Liu, Yang Yu, Kun Zhang. AAAI 2022.
• Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Roy Zohar, Shie Mannor, Guy Tennenholtz. AAAI 2022.
• Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems. [pdf]
  • Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Shiguang Wu. AAAI 2022.
• A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning. [pdf]
  • Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato. AAAI 2022.
• Goal Recognition as Reinforcement Learning. [pdf]
  • Leonardo Amado, Reuth Mirsky, Felipe Meneguzzi. AAAI 2022.
• NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming. [pdf]
  • Luke Kenworthy, Siddharth Nayak, Christopher Chin, Hamsa Balakrishnan. AAAI 2022.
• MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems. [pdf]
  • Zefang Zong, Meng Zheng, Yong Li, Depeng Jin. AAAI 2022.
• Eye of the Beholder: Improved Relation Generalization for Text-Based Reinforcement Learning Agents. [pdf]
  • Keerthiram Murugesan, Subhajit Chaudhury, Kartik Talamadupula. AAAI 2022.
• Text-Based Interactive Recommendation via Offline Reinforcement Learning. [pdf]
  • Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin. AAAI 2022.
• Multi-Agent Reinforcement Learning Controller to Maximize Energy Efficiency for Multi-Generator Industrial Wave Energy Converter. [pdf]
  • Soumyendu Sarkar, Vineet Gundecha, Alexander Shmakov, Sahand Ghorbanpour, Ashwin Ramesh Babu, Paolo Faraboschi, Mathieu Cocho, Alexandre Pichard, Jonathan Fievez. AAAI 2022.
• Bayesian Model-Based Offline Reinforcement Learning for Product Allocation. [pdf]
  • Porter Jenkins, Hua Wei, J. Stockton Jenkins, Zhenhui Li. AAAI 2022.
• Reinforcement Learning for Datacenter Congestion Control. [pdf]
  • Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor. AAAI 2022.
• Creating Interactive Crowds with Reinforcement Learning. [pdf]
  • Ariel Kwiatkowski. AAAI 2022.
• Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract). [pdf]
  • Hansin Ahuja, Lynnette Hui Xian Ng, Kokil Jaidka. AAAI 2022.
• Reinforcement Learning Explainability via Model Transforms (Student Abstract). [pdf]
  • Mira Finkelstein, Lucy Liu, Yoav Kolumbus, David C. Parkes, Jeffrey S. Rosenshein, Sarah Keren. AAAI 2022.
• Using Reinforcement Learning for Operating Educational Campuses Safely during a Pandemic (Student Abstract). [pdf]
  • Elizabeth Akinyi Ondula, Bhaskar Krishnamachari. AAAI 2022.
• Criticality-Based Advice in Reinforcement Learning (Student Abstract). [pdf]
  • Yitzhak Spielberg, Amos Azaria. AAAI 2022.
• VeNAS: Versatile Negotiating Agent Strategy via Deep Reinforcement Learning (Student Abstract). [pdf]
  • Toki Takahashi, Ryota Higa, Katsuhide Fujita, Shinji Nakadai. AAAI 2022.

International Conference on Autonomous Agents and Multiagent Systems

• Multi-Objective Reinforcement Learning with Non-Linear Scalarization. [pdf]
  • Mridul Agarwal, Vaneet Aggarwal, Tian Lan. AAMAS 2022.
• Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning. [pdf]
  • Parand Alizadeh Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith. AAMAS 2022.
• Unbiased Asymmetric Reinforcement Learning under Partial Observability. [pdf]
  • Andrea Baisero, Christopher Amato. AAMAS 2022.
• Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions. [pdf]
  • Tom Bewley, Freddy Lécué. AAMAS 2022.
• Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs. [pdf]
  • Mustafa Mert Çelikok, Frans A. Oliehoek, Samuel Kaski. AAMAS 2022.
• Individual-Level Inverse Reinforcement Learning for Mean Field Games. [pdf]
  • Yang Chen, Libo Zhang, Jiamou Liu, Shuyue Hu. AAMAS 2022.
• Scalable Multi-Agent Model-Based Reinforcement Learning. [pdf]
  • Vladimir Egorov, Alexey Shpilman. AAMAS 2022.
• Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. [pdf]
  • Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin. AAMAS 2022.
• Autonomous Swarm Shepherding Using Curriculum-Based Reinforcement Learning. [pdf]
  • Aya Hussein, Eleni Petraki, Sondoss Elsawah, Hussein A. Abbass. AAMAS 2022.
• Translating Omega-Regular Specifications to Average Objectives for Model-Free Reinforcement Learning. [pdf]
  • Milad Kazemi, Mateo Perez, Fabio Somenzi, Sadegh Soudjani, Ashutosh Trivedi, Alvaro Velasquez. AAMAS 2022.
• Disentangling Successor Features for Coordination in Multi-agent Reinforcement Learning. [pdf]
  • Seung Hyun Kim, Neale Van Stralen, Girish Chowdhary, Huy T. Tran. AAMAS 2022.
• CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents. [pdf]
  • Joe McCalmon, Thai Le, Sarra Alqahtani, Dongwon Lee. AAMAS 2022.
• Deep Reinforcement Learning for Active Wake Control. [pdf]
  • Grigory Neustroev, Sytze P. E. Andringa, Remco A. Verzijlbergh, Mathijs Michiel de Weerdt. AAMAS 2022.
• Characterizing Attacks on Deep Reinforcement Learning. [pdf]
  • Xinlei Pan, Chaowei Xiao, Warren He, Shuang Yang, Jian Peng, Mingjie Sun, Mingyan Liu, Bo Li, Dawn Song. AAMAS 2022.
• GCS: Graph-Based Coordination Strategy for Multi-Agent Reinforcement Learning. [pdf]
  • Jingqing Ruan, Yali Du, Xuantang Xiong, Dengpeng Xing, Xiyun Li, Linghui Meng, Haifeng Zhang, Jun Wang, Bo Xu. AAMAS 2022.
• Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration. [pdf]
  • Lukas Schäfer, Filippos Christianos, Josiah P. Hanna, Stefano V. Albrecht. AAMAS 2022.
• Sympathy-based Reinforcement Learning Agents. [pdf]
  • Manisha Senadeera, Thommen George Karimpanal, Sunil Gupta, Santu Rana. AAMAS 2022.
• Off-Policy Evolutionary Reinforcement Learning with Maximum Mutations. [pdf]
  • Karush Suri. AAMAS 2022.
• Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents. [pdf]
  • Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva. AAMAS 2022.
• Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning. [pdf]
  • Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran. AAMAS 2022.
• SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Zhiwei Xu, Yunpeng Bai, Dapeng Li, Bin Zhang, Guoliang Fan. AAMAS 2022.
• Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning. [pdf]
  • Wanqi Xue, Wei Qiu, Bo An, Zinovi Rabinovich, Svetlana Obraztsova, Chai Kiat Yeo. AAMAS 2022.
• Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning. [pdf]
  • Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha. AAMAS 2022.
• Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers. AAMAS 2022.
• Status-quo Policy Gradient in Multi-Agent Reinforcement Learning. [pdf]
  • Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy. AAMAS 2022.
• Adaptive Aggregation Weight Assignment for Federated Learning: A Deep Reinforcement Learning Approach. [pdf]
  • Enwei Guo, Xiumin Wang, Weiwei Wu. AAMAS 2022.
• Learning to Advise and Learning from Advice in Cooperative Multiagent Reinforcement Learning. [pdf]
  • Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang. AAMAS 2022.
• Improving Generalization with Cross-State Behavior Matching in Deep Reinforcement Learning. [pdf]
  • Guan-Ting Liu, Guan-Yu Lin, Pu-Jen Cheng. AAMAS 2022.
• Multimodal Reinforcement Learning with Effective State Representation Learning. [pdf]
  • Jinming Ma, Yingfeng Chen, Feng Wu, Xianpeng Ji, Yu Ding. AAMAS 2022.
• Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation. [pdf]
  • Henri Meess, Jeremias Gerner, Daniel Hein, Stefanie Schmidtner, Gordon Elger. AAMAS 2022.
• Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning. [pdf]
  • Jennifer She, Jayesh K. Gupta, Mykel J. Kochenderfer. AAMAS 2022.
• Environment Guided Interactive Reinforcement Learning: Learning from Binary Feedback in High-Dimensional Robot Task Environments. [pdf]
  • Isaac S. Sheidlower, Elaine Schaertl Short, Allison Moore. AAMAS 2022.
• Speeding up Deep Reinforcement Learning through Influence-Augmented Local Simulators. [pdf]
  • Miguel Suau, Jinke He, Matthijs T. J. Spaan, Frans A. Oliehoek. AAMAS 2022.
• Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning. [pdf]
  • Shang Wang, Mathieu Reymond, Athirai A. Irissappane, Diederik M. Roijers. AAMAS 2022.
• Performance of Deep Reinforcement Learning for High Frequency Market Making on Actual Tick Data. [pdf]
  • Ziyi Xu, Xue Cheng, Yangbo He. AAMAS 2022.
• Off-Policy Correction For Multi-Agent Reinforcement Learning. [pdf]
  • Michal Zawalski, Blazej Osinski, Henryk Michalewski, Piotr Milos. AAMAS 2022.
• Towards Anomaly Detection in Reinforcement Learning. [pdf]
  • Robert Müller, Steffen Illium, Thomy Phan, Tom Haider, Claudia Linnhoff-Popien. AAMAS 2022.
• Exploration and Communication for Partially Observable Collaborative Multi-Agent Reinforcement Learning. [pdf]
  • Raphaël Avalos. AAMAS 2022.
• Towards Multi-Agent Interactive Reinforcement Learning for Opportunistic Software Composition in Ambient Environments. [pdf]
  • Kevin Delcourt. AAMAS 2022.
• Model-free and Model-based Reinforcement Learning, the Intersection of Learning and Planning. [pdf]
  • Piotr Januszewski. AAMAS 2022.
• Task Generalisation in Multi-Agent Reinforcement Learning. [pdf]
  • Lukas Schäfer. AAMAS 2022.
• Empathetic Reinforcement Learning Agents. [pdf]
  • Manisha Senadeera. AAMAS 2022.

International Conference on Learning Representations

• The Information Geometry of Unsupervised Reinforcement Learning. [pdf]
  • Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine. ICLR 2022.
• CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing. [pdf]
  • Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, Bo Li. ICLR 2022.
• Generalisation in Lifelong Reinforcement Learning through Logical Composition. [pdf]
  • Geraud Nangue Tasse, Steven James, Benjamin Rosman. ICLR 2022.
• COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. [pdf]
  • Fan Wu, Linyi Li, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li. ICLR 2022.
• Should I Run Offline Reinforcement Learning or Behavioral Cloning? [pdf]
  • Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine. ICLR 2022.
• Learning State Representations via Retracing in Reinforcement Learning. [pdf]
  • Changmin Yu, Dong Li, Jianye Hao, Jun Wang, Neil Burgess. ICLR 2022.
• Distributional Reinforcement Learning with Monotonic Splines. [pdf]
  • Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart. ICLR 2022.
• Orchestrated Value Mapping for Reinforcement Learning. [pdf]
  • Mehdi Fatemi, Arash Tavakoli. ICLR 2022.
• A First-Occupancy Representation for Reinforcement Learning. [pdf]
  • Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani. ICLR 2022.
• Offline Reinforcement Learning with Implicit Q-Learning. [pdf]
  • Ilya Kostrikov, Ashvin Nair, Sergey Levine. ICLR 2022.
• Reward Uncertainty for Exploration in Preference-based Reinforcement Learning. [pdf]
  • Xinran Liang, Katherine Shu, Kimin Lee, Pieter Abbeel. ICLR 2022.
• Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning. [pdf]
  • Sunghoon Hong, Deunsol Yoon, Kee-Eung Kim. ICLR 2022.
• SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning. [pdf]
  • Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee. ICLR 2022.
• GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems. [pdf]
  • Youngsoo Jang, Jongmin Lee, Kee-Eung Kim. ICLR 2022.
• Learning a subspace of policies for online adaptation in Reinforcement Learning. [pdf]
  • Jean-Baptiste Gaya, Laure Soulier, Ludovic Denoyer. ICLR 2022.
• Reinforcement Learning in Presence of Discrete Markovian Context Evolution. [pdf]
  • Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou-Ammar. ICLR 2022.
• Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning. [pdf]
  • Yutong Wang, Ke Xue, Chao Qian. ICLR 2022.
• Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. [pdf]
  • Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang. ICLR 2022.
• Local Feature Swapping for Generalization in Reinforcement Learning. [pdf]
  • David Bertoin, Emmanuel Rachelson. ICLR 2022.
• Model-Based Offline Meta-Reinforcement Learning with Regularization. [pdf]
  • Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, Junshan Zhang. ICLR 2022.
• Offline Reinforcement Learning with Value-based Episodic Memory. [pdf]
  • Xiaoteng Ma, Yiqin Yang, Hao Hu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, Bin Liang, Qihan Liu. ICLR 2022.
• Bi-linear Value Networks for Multi-goal Reinforcement Learning. [pdf]
  • Zhang-Wei Hong, Ge Yang, Pulkit Agrawal. ICLR 2022.
• Maximizing Ensemble Diversity in Deep Reinforcement Learning. [pdf]
  • Hassam Sheikh, Mariano Phielipp, Ladislau Bölöni. ICLR 2022.
• Policy Smoothing for Provably Robust Reinforcement Learning. [pdf]
  • Aounon Kumar, Alexander Levine, Soheil Feizi. ICLR 2022.
• An Experimental Design Perspective on Model-Based Reinforcement Learning. [pdf]
  • Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger. ICLR 2022.
• Hindsight Foresight Relabeling for Meta-Reinforcement Learning. [pdf]
  • Michael Wan, Jian Peng, Tanmay Gangwani. ICLR 2022.
• Autonomous Reinforcement Learning: Formalism and Benchmarking. [pdf]
  • Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn. ICLR 2022.
• Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. [pdf]
  • Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto. ICLR 2022.
• Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities. [pdf]
  • Jianda Chen, Sinno Jialin Pan. ICLR 2022.
• Imitation Learning by Reinforcement Learning. [pdf]
  • Kamil Ciosek. ICLR 2022.
• On-Policy Model Errors in Reinforcement Learning. [pdf]
  • Lukas P. Fröhlich, Maksym Lefarov, Melanie N. Zeilinger, Felix Berkenkamp. ICLR 2022.
• DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning. [pdf]
  • Jinxin Liu, Hongyin Zhang, Donglin Wang. ICLR 2022.
• HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation. [pdf]
  • Boyan Li, Hongyao Tang, Yan Zheng, Jianye Hao, Pengyi Li, Zhen Wang, Zhaopeng Meng, Li Wang. ICLR 2022.
• Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. [pdf]
  • Masatoshi Uehara, Wen Sun. ICLR 2022.
• A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning. [pdf]
  • Jiaxian Guo, Mingming Gong, Dacheng Tao. ICLR 2022.
• Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning. [pdf]
  • Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen, Ying Wen, Fanglei Sun, Jun Wang, Yaodong Yang. ICLR 2022.
• HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning. [pdf]
  • Ziniu Li, Yingru Li, Yushun Zhang, Tong Zhang, Zhi-Quan Luo. ICLR 2022.
• On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. [pdf]
  • Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit. ICLR 2022.
• Pareto Policy Pool for Model-based Offline Reinforcement Learning. [pdf]
  • Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi. ICLR 2022.
• On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning. [pdf]
  • Che Wang, Shuhan Yuan, Kai Shao, Keith W. Ross. ICLR 2022.
• Modular Lifelong Reinforcement Learning via Neural Composition. [pdf]
  • Jorge A. Mendez, Harm van Seijen, Eric Eaton. ICLR 2022.
• Dropout Q-Functions for Doubly Efficient Reinforcement Learning. [pdf]
  • Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, Yoshimasa Tsuruoka. ICLR 2022.
• A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. [pdf]
  • Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon Shaolei Du. ICLR 2022.
• Skill-based Meta-Reinforcement Learning. [pdf]
  • Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, Joseph J. Lim. ICLR 2022.
• Know Your Action Set: Learning Action Relations for Reinforcement Learning. [pdf]
  • Ayush Jain, Norio Kosaka, Kyung-Min Kim, Joseph J. Lim. ICLR 2022.
• Boosted Curriculum Reinforcement Learning. [pdf]
  • Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen. ICLR 2022.
• Learning Synthetic Environments and Reward Networks for Reinforcement Learning. [pdf]
  • Fabio Ferreira, Thomas Nierhoff, Andreas Sälinger, Frank Hutter. ICLR 2022.
• Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory. [pdf]
  • Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang. ICLR 2022.
• DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. [pdf]
  • Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine. ICLR 2022.
• CoBERL: Contrastive BERT for Reinforcement Learning. [pdf]
  • Andrea Banino, Adrià Puigdomènech Badia, Jacob C. Walker, Tim Scholtes, Jovana Mitrovic, Charles Blundell. ICLR 2022.
• Value Gradient weighted Model-Based Reinforcement Learning. [pdf]
  • Claas Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand. ICLR 2022.
• Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration. [pdf]
  • Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh, Dileep M. Kalathil, Srinivas Shakkottai. ICLR 2022.
• AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. [pdf]
  • Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang. ICLR 2022.
• Revisiting Design Choices in Offline Model Based Reinforcement Learning. [pdf]
  • Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, Stephen J. Roberts. ICLR 2022.
• COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. [pdf]
  • Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez. ICLR 2022.
• Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward. [pdf]
  • Hairi, Jia Liu, Songtao Lu. ICLR 2022.
• $\mathrm{SO}(2)$-Equivariant Reinforcement Learning. [pdf]
  • Dian Wang, Robin Walters, Robert Platt. ICLR 2022.
• Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation. [pdf]
  • Vincent Mai, Kaustubh Mani, Liam Paull. ICLR 2022.
• On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning. [pdf]
  • Marc Aurel Vischer, Robert Tjarko Lange, Henning Sprekeler. ICLR 2022.
• Learning Altruistic Behaviours in Reinforcement Learning without External Rewards. [pdf]
  • Tim Franzmeyer, Mateusz Malinowski, João F. Henriques. ICLR 2022.
• Programmatic Reinforcement Learning without Oracles. [pdf]
  • Wenjie Qiu, He Zhu. ICLR 2022.
• Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning. [pdf]
  • Haichao Zhang, Wei Xu, Haonan Yu. ICLR 2022.
• Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. [pdf]
  • Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang. ICLR 2022.
• Understanding and Preventing Capacity Loss in Reinforcement Learning. [pdf]
  • Clare Lyle, Mark Rowland, Will Dabney. ICLR 2022.
• Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. [pdf]
  • Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu. ICLR 2022.

International Conference on Machine Learning

• EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning. [pdf]
  • Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang. ICML 2022.
• Optimizing Sequential Experimental Design with Deep Reinforcement Learning. [pdf]
  • Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli. ICML 2022.
• Interactive Inverse Reinforcement Learning for Cooperative Games. [pdf]
  • Thomas Kleine Büning, Anne-Marie George, Christos Dimitrakakis. ICML 2022.
• Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency. [pdf]
  • Qi Cai, Zhuoran Yang, Zhaoran Wang. ICML 2022.
• Stabilizing Off-Policy Deep Reinforcement Learning from Pixels. [pdf]
  • Edoardo Cetin, Philip J. Ball, Stephen J. Roberts, Oya Çeliktutan. ICML 2022.
• Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. [pdf]
  • Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang. ICML 2022.
• Adversarially Trained Actor Critic for Offline Reinforcement Learning. [pdf]
  • Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal. ICML 2022.
• Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning. [pdf]
  • Angelo Damiani, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli. ICML 2022.
• Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. [pdf]
  • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan. ICML 2022.
• DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations. [pdf]
  • Fei Deng, Ingook Jang, Sungjin Ahn. ICML 2022.
• Branching Reinforcement Learning. [pdf]
  • Yihan Du, Wei Chen. ICML 2022.
• Provable Reinforcement Learning with a Short-Term Memory. [pdf]
  • Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi. ICML 2022.
• DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck. [pdf]
  • Jiameng Fan, Wenchao Li. ICML 2022.
• Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning. [pdf]
  • Yingjie Fei, Ruitu Xu. ICML 2022.
• Fast Population-Based Reinforcement Learning on a Single Machine. [pdf]
  • Arthur Flajolet, Claire Bizon Monroc, Karim Beguir, Thomas Pierrot. ICML 2022.
• Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu. ICML 2022.
• Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning. [pdf]
  • Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, Igor Mordatch. ICML 2022.
• Retrieval-Augmented Reinforcement Learning. [pdf]
  • Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell. ICML 2022.
• The State of Sparse Training in Deep Reinforcement Learning. [pdf]
  • Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro. ICML 2022.
• Learning Pseudometric-based Action Representations for Offline Reinforcement Learning. [pdf]
  • Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, Bo An. ICML 2022.
• Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity. [pdf]
  • Lin Guan, Sarath Sreedharan, Subbarao Kambhampati. ICML 2022.
• Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes. [pdf]
  • Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang. ICML 2022.
• Off-Policy Reinforcement Learning with Delayed Rewards. [pdf]
  • Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng. ICML 2022.
• Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. [pdf]
  • Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine. ICML 2022.
• Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation. [pdf]
  • Pihe Hu, Yu Chen, Longbo Huang. ICML 2022.
• On the Role of Discount Factor in Offline Reinforcement Learning. [pdf]
  • Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang. ICML 2022.
• MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer. [pdf]
  • Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung. ICML 2022.
• Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling. [pdf]
  • Sajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri. ICML 2022.
• Curriculum Reinforcement Learning via Constrained Optimal Transport. [pdf]
  • Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen. ICML 2022.
• Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters. [pdf]
  • Vladislav Kurenkov, Sergey Kolesnikov. ICML 2022.
• Goal Misgeneralization in Deep Reinforcement Learning. [pdf]
  • Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger. ICML 2022.
• Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. [pdf]
  • Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist. ICML 2022.
• Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning. [pdf]
  • Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu. ICML 2022.
• Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning. [pdf]
  • Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao. ICML 2022.
• PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. [pdf]
  • Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang. ICML 2022.
• Delayed Reinforcement Learning by Imitation. [pdf]
  • Pierre Liotet, Davide Maran, Lorenzo Bisi, Marcello Restelli. ICML 2022.
• Constrained Variational Policy Optimization for Safe Reinforcement Learning. [pdf]
  • Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao. ICML 2022.
• Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. [pdf]
  • Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang. ICML 2022.
• Learning Dynamics and Generalization in Deep Reinforcement Learning. [pdf]
  • Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal. ICML 2022.
• Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. [pdf]
  • Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang. ICML 2022.
• On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning. [pdf]
  • Weichao Mao, Lin Yang, Kaiqing Zhang, Tamer Basar. ICML 2022.
• Optimizing Tensor Network Contraction Using Reinforcement Learning. [pdf]
  • Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik. ICML 2022.
• A Simple Reward-free Approach to Constrained Reinforcement Learning. [pdf]
  • Sobhan Miryoosefi, Chi Jin. ICML 2022.
• EqR: Equivariant Representations for Data-Efficient Reinforcement Learning. [pdf]
  • Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh. ICML 2022.
• The Primacy Bias in Deep Reinforcement Learning. [pdf]
  • Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville. ICML 2022.
• History Compression via Language Models in Reinforcement Learning. [pdf]
  • Fabian Paischer, Thomas Adler, Vihang P. Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner, Hamid Eghbal-Zadeh, Sepp Hochreiter. ICML 2022.
• Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification. [pdf]
  • Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu. ICML 2022.
• Offline Meta-Reinforcement Learning with Online Self-Supervision. [pdf]
  • Vitchyr H. Pong, Ashvin V. Nair, Laura M. Smith, Catherine Huang, Sergey Levine. ICML 2022.
• Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. [pdf]
  • Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang. ICML 2022.
• Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. [pdf]
  • Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang. ICML 2022.
• Direct Behavior Specification via Constrained Reinforcement Learning. [pdf]
  • Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal. ICML 2022.
• Reinforcement Learning with Action-Free Pre-Training from Videos. [pdf]
  • Younggyo Seo, Kimin Lee, Stephen L. James, Pieter Abbeel. ICML 2022.
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation. [pdf]
  • Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause. ICML 2022.
• A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning. [pdf]
  • Archit Sharma, Rehaan Ahmad, Chelsea Finn. ICML 2022.
• DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning. [pdf]
  • Hassam Sheikh, Kizza Frisbee, Mariano Phielipp. ICML 2022.
• Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity. [pdf]
  • Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi. ICML 2022.
• Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning. [pdf]
  • Kyunghwan Son, Junsu Kim, Sungsoo Ahn, Roben Delos Reyes, Yung Yi, Jinwoo Shin. ICML 2022.
• Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation. [pdf]
  • Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Henry Mguni, Jun Wang, Haitham Ammar. ICML 2022.
• Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments. [pdf]
  • Ryan Sullivan, Jordan K. Terry, Benjamin Black, John P. Dickerson. ICML 2022.
• Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning. [pdf]
  • Yunhao Tang. ICML 2022.
• Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. [pdf]
  • Adam R. Villaflor, Zhe Huang, Swapnil Pande, John M. Dolan, Jeff Schneider. ICML 2022.
• First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. [pdf]
  • Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson. ICML 2022.
• Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four. [pdf]
  • Stephan Wäldchen, Sebastian Pokutta, Felix Huber. ICML 2022.
• Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning. [pdf]
  • Lipeng Wan, Zeyang Liu, Xingyu Chen, Xuguang Lan, Nanning Zheng. ICML 2022.
• Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods. [pdf]
  • Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen. ICML 2022.
• Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search. [pdf]
  • Qi Wang, Herke van Hoof. ICML 2022.
• Individual Reward Assisted Multi-Agent Reinforcement Learning. [pdf]
  • Li Wang, Yupeng Zhang, Yujing Hu, Weixun Wang, Chongjie Zhang, Yang Gao, Jianye Hao, Tangjie Lv, Changjie Fan. ICML 2022.
• Policy Gradient Method For Robust Reinforcement Learning. [pdf]
  • Yue Wang, Shaofeng Zou. ICML 2022.
• Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. [pdf]
  • Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara. ICML 2022.
• Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning. [pdf]
  • Harley E. Wiltzer, David Meger, Marc G. Bellemare. ICML 2022.
• Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum. [pdf]
  • Junlin Wu, Yevgeniy Vorobeychik. ICML 2022.
• Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning. [pdf]
  • Shentao Yang, Yihao Feng, Shujian Zhang, Mingyuan Zhou. ICML 2022.
• How to Leverage Unlabeled Data in Offline Reinforcement Learning. [pdf]
  • Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine. ICML 2022.
• Reachability Constrained Reinforcement Learning. [pdf]
  • Dongjie Yu, Haitong Ma, Sheng-bo Li, Jianyu Chen. ICML 2022.
• Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning. [pdf]
  • Sixing Yu, Arya Mazaheri, Ali Jannesari. ICML 2022.
• Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning. [pdf]
  • Haoqi Yuan, Zongqing Lu. ICML 2022.
• Actor-Critic based Improper Reinforcement Learning. [pdf]
  • Mohammadi Zaki, Avi Mohan, Aditya Gopalan, Shie Mannor. ICML 2022.
• Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. [pdf]
  • Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun. ICML 2022.
• A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines. [pdf]
  • Weichao Zhou, Wenchao Li. ICML 2022.

International Conference on Robotics and Automation

• Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning. [pdf]
  • Ramzi Ourari, Kai Cui, Ahmed Elshamanhory, Heinz Koeppl. ICRA 2022.
• Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning. [pdf]
  • Se-Wook Yoo, Seung-Woo Seo. ICRA 2022.
• OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. [pdf]
  • Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota. ICRA 2022.
• AMI: Adaptive Motion Imitation Algorithm Based on Deep Reinforcement Learning. [pdf]
  • Nazita Taghavi, Moath H. A. Alqatamin, Dan O. Popa. ICRA 2022.
• Decentralized Ride-sharing of Shared Autonomous Vehicles Using Graph Neural Network-Based Reinforcement Learning. [pdf]
  • Boqi Li, Nejib Ammar, Prashant Tiwari, Huei Peng. ICRA 2022.
• Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning. [pdf]
  • Yunke Ao, Le Chen, Florian Tschopp, Michel Breyer, Roland Siegwart, Andrei Cramariuc. ICRA 2022.
• Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning. [pdf]
  • Yuzi Yan, Xiaoxiang Li, Xinyou Qiu, Jiantao Qiu, Jian Wang, Yu Wang, Yuan Shen. ICRA 2022.
• Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications. [pdf]
  • Xiangyu Zeng, Tobias Zaenker, Maren Bennewitz. ICRA 2022.
• Real-Robot Deep Reinforcement Learning: Improving Trajectory Tracking of Flexible-Joint Manipulator with Reference Correction. [pdf]
  • Dmytro Pavlichenko, Sven Behnke. ICRA 2022.
• Discovering Synergies for Robot Manipulation with Multi-Task Reinforcement Learning. [pdf]
  • Zhanpeng He, Matei T. Ciocarlie. ICRA 2022.
• Personalized Car Following for Autonomous Driving with Inverse Reinforcement Learning. [pdf]
  • Zhouqiao Zhao, Ziran Wang, Kyungtae Han, Rohit Gupta, Prashant Tiwari, Guoyuan Wu, Matthew J. Barth. ICRA 2022.
• Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning. [pdf]
  • Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel. ICRA 2022.
• Intrinsically Motivated Self-supervised Learning in Reinforcement Learning. [pdf]
  • Yue Zhao, Chenzhuang Du, Hang Zhao, Tiejun Li. ICRA 2022.
• Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning. [pdf]
  • Jun Jin, Daniel Graves, Cameron Haigh, Jun Luo, Martin Jägersand. ICRA 2022.
• Exploiting Abstract Symmetries in Reinforcement Learning for Complex Environments. [pdf]
  • Kashish Gupta, Homayoun Najjaran. ICRA 2022.
• Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing. [pdf]
  • Julius Rückin, Liren Jin, Marija Popovic. ICRA 2022.
• Learning to Rock-and-Walk: Dynamic, Non-Prehensile, and Underactuated Object Locomotion Through Reinforcement Learning. [pdf]
  • Abdullah Nazir, Xu Pu, Juan Rojas, Jungwon Seo. ICRA 2022.
• Learning Crowd-Aware Robot Navigation from Challenging Environments via Distributed Deep Reinforcement Learning. [pdf]
  • Sango Matsuzaki, Yuji Hasegawa. ICRA 2022.
• ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation. [pdf]
  • Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How. ICRA 2022.
• Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning. [pdf]
  • Sheng Li, Yutai Zhou, Ross E. Allen, Mykel J. Kochenderfer. ICRA 2022.
• Enhancing Deep Reinforcement Learning Approaches for Multi-Robot Navigation via Single-Robot Evolutionary Policy Search. [pdf]
  • Enrico Marchesini, Alessandro Farinelli. ICRA 2022.
• Barrier Function-based Safe Reinforcement Learning for Formation Control of Mobile Robots. [pdf]
  • Xinglong Zhang, Yaoqian Peng, Wei Pan, Xin Xu, Haibin Xie. ICRA 2022.
• Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots. [pdf]
  • Yufeng Yuan, A. Rupam Mahmood. ICRA 2022.
• From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agent. [pdf]
  • Ganghun Lee, Minji Kim, Min Su Lee, Byoung-Tak Zhang. ICRA 2022.
• Robust Reinforcement Learning via Genetic Curriculum. [pdf]
  • Yeeho Song, Jeff Schneider. ICRA 2022.
• Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning. [pdf]
  • Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha. ICRA 2022.
• A Deep Reinforcement Learning Environment for Particle Robot Navigation and Object Manipulation. [pdf]
  • Jeremy Shen, Erdong Xiao, Yuchen Liu, Chen Feng. ICRA 2022.
• Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments. [pdf]
  • Jakob Thumm, Matthias Althoff. ICRA 2022.
• Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors. [pdf]
  • Hoang-Giang Cao, Weihao Zeng, I-Chen Wu. ICRA 2022.
• Offline Meta-Reinforcement Learning for Industrial Insertion. [pdf]
  • Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz, Stefan Schaal, Sergey Levine. ICRA 2022.
• HR-Planner: A Hierarchical Highway Tactical Planner based on Residual Reinforcement Learning. [pdf]
  • Haoran Wu, Yueyuan Li, Hanyang Zhuang, Chunxiang Wang, Ming Yang. ICRA 2022.
• Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks. [pdf]
  • Soroush Nasiriany, Huihan Liu, Yuke Zhu. ICRA 2022.
• RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning. [pdf]
  • Adarsh Kumar Kosta, Malik Aqeel Anwar, Priyadarshini Panda, Arijit Raychowdhury, Kaushik Roy. ICRA 2022.
• ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning. [pdf]
  • Sean Chen, Jensen Gao, Siddharth Reddy, Glen Berseth, Anca D. Dragan, Sergey Levine. ICRA 2022.
• Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning. [pdf]
  • K. Niranjan Kumar, Irfan Essa, Sehoon Ha. ICRA 2022.
• Promoting Quality and Diversity in Population-based Reinforcement Learning via Hierarchical Trajectory Space Exploration. [pdf]
  • Jiayu Miao, Tianze Zhou, Kun Shao, Ming Zhou, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang. ICRA 2022.
• Deep Drifting: Autonomous Drifting of Arbitrary Trajectories using Deep Reinforcement Learning. [pdf]
  • Fabian Domberg, Carlos Castelar Wembers, Hiren Patel, Georg Schildbach. ICRA 2022.
• Confidence-Based Robot Navigation Under Sensor Occlusion with Deep Reinforcement Learning. [pdf]
  • Hyeongyeol Ryu, Minsung Yoon, Daehyung Park, Sung-Eui Yoon. ICRA 2022.
• Visuotactile-RL: Learning Multimodal Manipulation Policies with Deep Reinforcement Learning. [pdf]
  • Johanna Hansen, Francois Robert Hogan, Dmitriy Rivkin, David Meger, Michael Jenkin, Gregory Dudek. ICRA 2022.
• Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles. [pdf]
  • Songyang Han, He Wang, Sanbao Su, Yuanyuan Shi, Fei Miao. ICRA 2022.
• Multi-Target Encirclement with Collision Avoidance via Deep Reinforcement Learning using Relational Graphs. [pdf]
  • Tianle Zhang, Zhen Liu, Zhiqiang Pu, Jianqiang Yi. ICRA 2022.
• Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach. [pdf]
  • Minghao Li, Yingrui Jie, Yang Kong, Hui Cheng. ICRA 2022.
• Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning. [pdf]
  • Zheng Zhang, Xiaohan Wang, Qingrui Zhang, Tianjiang Hu. ICRA 2022.
• TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning. [pdf]
  • Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Utsav Patel, Dinesh Manocha. ICRA 2022.
• Reinforcement Learning as a Method for Tuning CPG Controllers for Underwater Multi-Fin Propulsion. [pdf]
  • Anthony Drago, Gabe N. Carryon, James L. Tangorra. ICRA 2022.

International Joint Conference on Artificial Intelligence

• Toward Policy Explanations for Multi-Agent Reinforcement Learning. [pdf]
  • Kayla Boggess, Sarit Kraus, Lu Feng. IJCAI 2022.
• Search-Based Testing of Reinforcement Learning. [pdf]
  • Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig, Bettina Könighofer. IJCAI 2022.
• Fast and Fine-grained Autoscaler for Streaming Jobs with Reinforcement Learning. [pdf]
  • Mingzhe Xing, Hangyu Mao, Zhen Xiao. IJCAI 2022.
• AttExplainer: Explain Transformer via Attention by Reinforcement Learning. [pdf]
  • Runliang Niu, Zhepei Wei, Yan Wang, Qi Wang. IJCAI 2022.
• Feature and Instance Joint Selection: A Reinforcement Learning Perspective. [pdf]
  • Wei Fan, Kunpeng Liu, Hao Liu, Hengshu Zhu, Hui Xiong, Yanjie Fu. IJCAI 2022.
• Reinforcement Learning with Option Machines. [pdf]
  • Floris den Hengst, Vincent François-Lavet, Mark Hoogendoorn, Frank van Harmelen. IJCAI 2022.
• A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification. [pdf]
  • Ge Gao, Qitong Gao, Xi Yang, Miroslav Pajic, Min Chi. IJCAI 2022.
• Leveraging Class Abstraction for Commonsense Reinforcement Learning via Residual Policy Gradient Methods. [pdf]
  • Niklas Höpner, Ilaria Tiddi, Herke van Hoof. IJCAI 2022.
• Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training. [pdf]
  • Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao. IJCAI 2022.
• Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems. [pdf]
  • Rushang Karia, Siddharth Srivastava. IJCAI 2022.
• Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning. [pdf]
  • Kyungsoo Kim, Jeongsoo Ha, Yusung Kim. IJCAI 2022.
• JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning. [pdf]
  • Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang. IJCAI 2022.
• Search-based Reinforcement Learning through Bandit Linear Optimization. [pdf]
  • Milan Peelman, Antoon Bronselaer, Guy De Tré. IJCAI 2022.
• Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning. [pdf]
  • Anshuka Rangi, Haifeng Xu, Long Tran-Thanh, Massimo Franceschetti. IJCAI 2022.
• Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes. [pdf]
  • Alessandro Ronca, Gabriel Paludo Licks, Giuseppe De Giacomo. IJCAI 2022.
• Lexicographic Multi-Objective Reinforcement Learning. [pdf]
  • Joar Skalse, Lewis Hammond, Charlie Griffin, Alessandro Abate. IJCAI 2022.
• Dynamic Sparse Training for Deep Reinforcement Learning. [pdf]
  • Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, Peter Stone. IJCAI 2022.
• CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning. [pdf]
  • Chenyu Sun, Hangwei Qian, Chunyan Miao. IJCAI 2022.
• On the (In)Tractability of Reinforcement Learning for LTL Objectives. [pdf]
  • Cambridge Yang, Michael L. Littman, Michael Carbin. IJCAI 2022.
• Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble. [pdf]
  • Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li. IJCAI 2022.
• Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk. [pdf]
  • Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu. IJCAI 2022.
• Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning. [pdf]
  • Zhecheng Yuan, Guozheng Ma, Yao Mu, Bo Xia, Bo Yuan, Xueqian Wang, Ping Luo, Huazhe Xu. IJCAI 2022.
• Penalized Proximal Policy Optimization for Safe Reinforcement Learning. [pdf]
  • Linrui Zhang, Li Shen, Long Yang, Shixiang Chen, Xueqian Wang, Bo Yuan, Dacheng Tao. IJCAI 2022.
• Multi-Constraint Deep Reinforcement Learning for Smooth Action Control. [pdf]
  • Guangyuan Zou, Ying He, F. Richard Yu, Longquan Chen, Weike Pan, Zhong Ming. IJCAI 2022.
• Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method. [pdf]
  • Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun, Baihua Zheng. IJCAI 2022.
• Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution. [pdf]
  • Feiyang Pan, Tongzhe Zhang, Ling Luo, Jia He, Shuoling Liu. IJCAI 2022.
• Exploring the Vulnerability of Deep Reinforcement Learning-based Emergency Control for Low Carbon Power Systems. [pdf]
  • Xu Wan, Lanting Zeng, Mingyang Sun. IJCAI 2022.
• Reinforcement Learning for Cross-Domain Hyper-Heuristics. [pdf]
  • Florian Mischek, Nysret Musliu. IJCAI 2022.
• Detect, Understand, Act: A Neuro-Symbolic Hierarchical Reinforcement Learning Framework (Extended Abstract). [pdf]
  • Ludovico Mitchener, David Tuckey, Matthew Crosby, Alessandra Russo. IJCAI 2022.
• Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach (Extended Abstract). [pdf]
  • Ashish Sharma, Inna W. Lin, Adam S. Miner, Dave C. Atkins, Tim Althoff. IJCAI 2022.
• Goal-Conditioned Reinforcement Learning: Problems and Solutions. [pdf]
  • Minghuan Liu, Menghui Zhu, Weinan Zhang. IJCAI 2022.
• Abstraction for Deep Reinforcement Learning. [pdf]
  • Murray Shanahan, Melanie Mitchell. IJCAI 2022.
• Interactive Reinforcement Learning for Symbolic Regression from Multi-Format Human-Preference Feedbacks. [pdf]
  • Laure Crochepierre, Lydia Boudjeloud-Assala, Vincent Barbesant. IJCAI 2022.

Annual Conference on Neural Information Processing Systems

• Adaptive Interest for Emphatic Reinforcement Learning. [pdf]
  • Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola. NeurIPS 2022.
• Offline Multi-Agent Reinforcement Learning with Knowledge Distillation. [pdf]
  • Wei-Cheng Tseng, Tsun-Hsuan Johnson Wang, Yen-Chen Lin, Phillip Isola. NeurIPS 2022.
• Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression. [pdf]
  • Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani. NeurIPS 2022.
• Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief. [pdf]
  • Kaiyang Guo, Yunfeng Shao, Yanhui Geng. NeurIPS 2022.
• Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning. [pdf]
  • Paul Rolland, Luca Viano, Norman Schürhoff, Boris Nikolov, Volkan Cevher. NeurIPS 2022.
• Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. [pdf]
  • Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun. NeurIPS 2022.
• Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. [pdf]
  • Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu. NeurIPS 2022.
• DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning. [pdf]
  • Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-François Chamberland. NeurIPS 2022.
• LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Mingyu Yang, Jian Zhao, Xunhan Hu, Wengang Zhou, Jiangcheng Zhu, Houqiang Li. NeurIPS 2022.
• Mildly Conservative Q-Learning for Offline Reinforcement Learning. [pdf]
  • Jiafei Lyu, Xiaoteng Ma, Xiu Li, Zongqing Lu. NeurIPS 2022.
• Reinforcement Learning with Automated Auxiliary Loss Search. [pdf]
  • Tairan He, Yuge Zhang, Kan Ren, Minghuan Liu, Che Wang, Weinan Zhang, Yuqing Yang, Dongsheng Li. NeurIPS 2022.
• FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. [pdf]
  • Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo. NeurIPS 2022.
• Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards. [pdf]
  • Ashwinkumar Badanidiyuru Varadaraja, Zhe Feng, Tianxi Li, Haifeng Xu. NeurIPS 2022.
• Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations. [pdf]
  • Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel S. Brown, Ken Goldberg. NeurIPS 2022.
• Open-Ended Reinforcement Learning with Neural Reward Functions. [pdf]
  • Robert Meier, Asier Mujika. NeurIPS 2022.
• Towards Safe Reinforcement Learning with a Safety Editor Policy. [pdf]
  • Haonan Yu, Wei Xu, Haichao Zhang. NeurIPS 2022.
• Sustainable Online Reinforcement Learning for Auto-bidding. [pdf]
  • Zhiyu Mou, Yusen Huo, Rongquan Bai, Mingzhou Xie, Chuan Yu, Jian Xu, Bo Zheng. NeurIPS 2022.
• Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments. [pdf]
  • Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai. NeurIPS 2022.
• Bellman Residual Orthogonalization for Offline Reinforcement Learning. [pdf]
  • Andrea Zanette, Martin J. Wainwright. NeurIPS 2022.
• Online Reinforcement Learning for Mixed Policy Scopes. [pdf]
  • Junzhe Zhang, Elias Bareinboim. NeurIPS 2022.
• Reinforcement Learning with Non-Exponential Discounting. [pdf]
  • Matthias Schultheis, Constantin A. Rothkopf, Heinz Koeppl. NeurIPS 2022.
• Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning. [pdf]
  • Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex M. Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes. NeurIPS 2022.
• A Policy-Guided Imitation Approach for Offline Reinforcement Learning. [pdf]
  • Haoran Xu, Li Jiang, Jianxiong Li, Xianyuan Zhan. NeurIPS 2022.
• Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning. [pdf]
  • Yuchen Xiao, Weihao Tan, Christopher Amato. NeurIPS 2022.
• Challenging Common Assumptions in Convex Reinforcement Learning. [pdf]
  • Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli. NeurIPS 2022.
• Constrained GPI for Zero-Shot Transfer in Reinforcement Learning. [pdf]
  • Jaekyeom Kim, Seohong Park, Gunhee Kim. NeurIPS 2022.
• Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration. [pdf]
  • Fanghui Liu, Luca Viano, Volkan Cevher. NeurIPS 2022.
• Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning. [pdf]
  • Yuanpei Chen, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuan Jiang, Zongqing Lu, Stephen McAleer, Hao Dong, Song-Chun Zhu, Yaodong Yang. NeurIPS 2022.
• ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization. [pdf]
  • Siqi Shen, Mengwei Qiu, Jun Liu, Weiquan Liu, Yongquan Fu, Xinwang Liu, Cheng Wang. NeurIPS 2022.
• Self-Organized Group for Cooperative Multi-agent Reinforcement Learning. [pdf]
  • Jianzhun Shao, Zhiqiang Lou, Hongchang Zhang, Yuhang Jiang, Shuncheng He, Xiangyang Ji. NeurIPS 2022.
• Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards. [pdf]
  • Rati Devidze, Parameswaran Kamalaruban, Adish Singla. NeurIPS 2022.
• Active Exploration for Inverse Reinforcement Learning. [pdf]
  • David Lindner, Andreas Krause, Giorgia Ramponi. NeurIPS 2022.
• Disentangling Transfer in Continual Reinforcement Learning. [pdf]
  • Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos. NeurIPS 2022.
• Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability. [pdf]
  • Whiyoung Jung, Myungsik Cho, Jongeui Park, Youngchul Sung. NeurIPS 2022.
• Grounded Reinforcement Learning: Learning to Win the Game under Human Commands. [pdf]
  • Shusheng Xu, Huaijie Wang, Yi Wu. NeurIPS 2022.
• CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations. [pdf]
  • Kai Yan, Alexander G. Schwing, Yu-Xiong Wang. NeurIPS 2022.
• Meta-Reinforcement Learning with Self-Modifying Networks. [pdf]
  • Mathieu Chalvidal, Thomas Serre, Rufin VanRullen. NeurIPS 2022.
• Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs. [pdf]
  • Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann. NeurIPS 2022.
• Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. [pdf]
  • Dilip Arumugam, Benjamin Van Roy. NeurIPS 2022.
• A Deep Reinforcement Learning Framework for Column Generation. [pdf]
  • Cheng Chi, Amine Mohamed Aboussalah, Elias B. Khalil, Juyoung Wang, Zoha Sherkat-Masoumi. NeurIPS 2022.
• Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees. [pdf]
  • Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong. NeurIPS 2022.
• A Unified Diversity Measure for Multiagent Reinforcement Learning. [pdf]
  • Zongkai Liu, Chao Yu, Yaodong Yang, Peng Sun, Zifan Wu, Yuan Li. NeurIPS 2022.
• Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation. [pdf]
  • Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao. NeurIPS 2022.
• Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. [pdf]
  • Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard. NeurIPS 2022.
• Understanding the Evolution of Linear Regions in Deep Reinforcement Learning. [pdf]
  • Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne. NeurIPS 2022.
• Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning. [pdf]
  • Dingwen Kong, Lin Yang. NeurIPS 2022.
• Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Zhiwei Xu, Dapeng Li, Bin Zhang, Yuan Zhan, Yunpeng Bai, Guoliang Fan. NeurIPS 2022.
• S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning. [pdf]
  • Daesol Cho, Dongseok Shim, H. Jin Kim. NeurIPS 2022.
• Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus. [pdf]
  • Qiwen Cui, Simon S. Du. NeurIPS 2022.
• Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning. [pdf]
  • Hua Wei, Jingxiao Chen, Xiyang Ji, Hongyang Qin, Minwen Deng, Siqin Li, Liang Wang, Weinan Zhang, Yong Yu, Liu Lin, Lanxiao Huang, Deheng Ye, Qiang Fu, Wei Yang. NeurIPS 2022.
• Value Function Decomposition for Iterative Design of Reinforcement Learning Agents. [pdf]
  • James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, Peter Stone. NeurIPS 2022.
• E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance. [pdf]
  • Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu. NeurIPS 2022.
• Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning. [pdf]
  • Jifeng Hu, Yanchao Sun, Hechang Chen, Sili Huang, Haiyin Piao, Yi Chang, Lichao Sun. NeurIPS 2022.
• Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning. [pdf]
  • Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, Huazhe Xu. NeurIPS 2022.
• Shield Decentralization for Safe Multi-Agent Reinforcement Learning. [pdf]
  • Daniel Melcer, Christopher Amato, Stavros Tripakis. NeurIPS 2022.
• Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning. [pdf]
  • Ruida Zhou, Tao Liu, Dileep Kalathil, P. R. Kumar, Chao Tian. NeurIPS 2022.
• Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach. [pdf]
  • Zohar Rimon, Aviv Tamar, Gilad Adler. NeurIPS 2022.
• DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning. [pdf]
  • Seungjae Lee, Jigang Kim, Inkyu Jang, H. Jin Kim. NeurIPS 2022.
• Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning. [pdf]
  • Chenyang Wu, Tianci Li, Zongzhang Zhang, Yang Yu. NeurIPS 2022.
• Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space. [pdf]
  • Jonatha Anselmi, Bruno Gaujal, Louis-Sébastien Rebuffi. NeurIPS 2022.
• Provable Defense against Backdoor Policies in Reinforcement Learning. [pdf]
  • Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, Jerry Zhu. NeurIPS 2022.
• You Only Live Once: Single-Life Reinforcement Learning. [pdf]
  • Annie S. Chen, Archit Sharma, Sergey Levine, Chelsea Finn. NeurIPS 2022.
• Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. [pdf]
  • Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill. NeurIPS 2022.
• On Gap-dependent Bounds for Offline Reinforcement Learning. [pdf]
  • Xinqi Wang, Qiwen Cui, Simon S. Du. NeurIPS 2022.
• GriddlyJS: A Web IDE for Reinforcement Learning. [pdf]
  • Christopher Bamford, Minqi Jiang, Mikayel Samvelyan, Tim Rocktäschel. NeurIPS 2022.
• Learning to Share in Networked Multi-Agent Reinforcement Learning. [pdf]
  • Yuxuan Yi, Ge Li, Yaowei Wang, Zongqing Lu. NeurIPS 2022.
• Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation. [pdf]
  • Zhizhou Ren, Anji Liu, Yitao Liang, Jian Peng, Jianzhu Ma. NeurIPS 2022.
• PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning. [pdf]
  • Hanhan Zhou, Tian Lan, Vaneet Aggarwal. NeurIPS 2022.
• Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning. [pdf]
  • Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo. NeurIPS 2022.
• RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning. [pdf]
  • Marc Rigter, Bruno Lacerda, Nick Hawes. NeurIPS 2022.
• Inherently Explainable Reinforcement Learning in Natural Language. [pdf]
  • Xiangyu Peng, Mark O. Riedl, Prithviraj Ammanabrolu. NeurIPS 2022.
• On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting. [pdf]
  • Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman. NeurIPS 2022.
• Multi-Agent Reinforcement Learning is a Sequence Modeling Problem. [pdf]
  • Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang. NeurIPS 2022.
• When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning. [pdf]
  • Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn. NeurIPS 2022.
• Reinforcement Learning with Neural Radiance Fields. [pdf]
  • Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint. NeurIPS 2022.
• Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. [pdf]
  • Qinghua Liu, Csaba Szepesvári, Chi Jin. NeurIPS 2022.
• DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning. [pdf]
  • Yuko Ishiwaka, Xiao S. Zeng, Shun Ogawa, Donovan Westwater, Tadayuki Tone, Masaki Nakada. NeurIPS 2022.
• Influencing Long-Term Behavior in Multiagent Reinforcement Learning. [pdf]
  • Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How. NeurIPS 2022.
• Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning. [pdf]
  • Guy Tennenholtz, Shie Mannor. NeurIPS 2022.
• Low-Rank Modular Reinforcement Learning via Muscle Synergy. [pdf]
  • Heng Dong, Tonghan Wang, Jiayuan Liu, Chongjie Zhang. NeurIPS 2022.
• GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis. [pdf]
  • Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang, Yan Zheng, Yi Li, Jianye Hao, Yang Liu. NeurIPS 2022.
• Faster Deep Reinforcement Learning with Slower Online Network. [pdf]
  • Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola. NeurIPS 2022.
• Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. [pdf]
  • Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang. NeurIPS 2022.
• Causality-driven Hierarchical Structure Discovery for Reinforcement Learning. [pdf]
  • Shaohui Peng, Xing Hu, Rui Zhang, Ke Tang, Jiaming Guo, Qi Yi, Ruizhi Chen, Xishan Zhang, Zidong Du, Ling Li, Qi Guo, Yunji Chen. NeurIPS 2022.
• Large-Scale Retrieval for Reinforcement Learning. [pdf]
  • Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap. NeurIPS 2022.
• Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game. [pdf]
  • Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart. NeurIPS 2022.
• Spectrum Random Masking for Generalization in Image-based Reinforcement Learning. [pdf]
  • Yangru Huang, Peixi Peng, Yifan Zhao, Guangyao Chen, Yonghong Tian. NeurIPS 2022.
• SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning. [pdf]
  • Yuhang Jiang, Jianzhun Shao, Shuncheng He, Hongchang Zhang, Xiangyang Ji. NeurIPS 2022.
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning. [pdf]
  • Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven Chu-Hong Hoi. NeurIPS 2022.
• PaCo: Parameter-Compositional Multi-task Reinforcement Learning. [pdf]
  • Lingfeng Sun, Haichao Zhang, Wei Xu, Masayoshi Tomizuka. NeurIPS 2022.
• Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning. [pdf]
  • Runze Liu, Fengshuo Bai, Yali Du, Yaodong Yang. NeurIPS 2022.
• EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine. [pdf]
  • Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, Shuicheng Yan. NeurIPS 2022.
• Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning. [pdf]
  • Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Furong Huang. NeurIPS 2022.
• When to Update Your Model: Constrained Model-based Reinforcement Learning. [pdf]
  • Tianying Ji, Yu Luo, Fuchun Sun, Mingxuan Jing, Fengxiang He, Wenbing Huang. NeurIPS 2022.
• RORL: Robust Offline Reinforcement Learning via Conservative Smoothing. [pdf]
  • Rui Yang, Chenjia Bai, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, Lei Han. NeurIPS 2022.
• The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning. [pdf]
  • Vindula Jayawardana, Catherine Tang, Sirui Li, Dajiang Suo, Cathy Wu. NeurIPS 2022.
• Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm. [pdf]
  • Ashish Kumar Jayant, Shalabh Bhatnagar. NeurIPS 2022.
• Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning. [pdf]
  • Arsène Fansi Tchango, Rishab Goel, Julien Martel, Zhi Wen, Gaétan Marceau-Caron, Joumana Ghosn. NeurIPS 2022.
• Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning. [pdf]
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• NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning. [pdf]
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• Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions. [pdf]
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• Mask-based Latent Reconstruction for Reinforcement Learning. [pdf]
  • Tao Yu, Zhizheng Zhang, Cuiling Lan, Yan Lu, Zhibo Chen. NeurIPS 2022.
• Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning. [pdf]
  • Shenao Zhang. NeurIPS 2022.
• Distributionally Adaptive Meta Reinforcement Learning. [pdf]
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• TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning. [pdf]
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• A Mixture Of Surprises for Unsupervised Reinforcement Learning. [pdf]
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• Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning. [pdf]
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• ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. [pdf]
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• DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning. [pdf]
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• MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control. [pdf]
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• Receding Horizon Inverse Reinforcement Learning. [pdf]
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• Oracle Inequalities for Model Selection in Offline Reinforcement Learning. [pdf]
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• Exponential Family Model-Based Reinforcement Learning via Score Matching. [pdf]
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• Regret Bounds for Information-Directed Reinforcement Learning. [pdf]
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• Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. [pdf]
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• Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior. [pdf]
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• Robust Imitation via Mirror Descent Inverse Reinforcement Learning. [pdf]
  • Dong-Sig Han, Hyunseo Kim, Hyundo Lee, Je-Hwan Ryu, Byoung-Tak Zhang. NeurIPS 2022.
• The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. [pdf]
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• On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning. [pdf]
  • Shiro Takagi. NeurIPS 2022.
• Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning. [pdf]
  • David Bertoin, Adil Zouitine, Mehdi Zouitine, Emmanuel Rachelson. NeurIPS 2022.
• Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels? [pdf]
  • Xiang Li, Jinghuan Shang, Srijan Das, Michael S. Ryoo. NeurIPS 2022.
• Distributional Reinforcement Learning for Risk-Sensitive Policies. [pdf]
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• A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. [pdf]
  • Bo Liu, Xidong Feng, Jie Ren, Luo Mai, Rui Zhu, Haifeng Zhang, Jun Wang, Yaodong Yang. NeurIPS 2022.
• Supported Policy Optimization for Offline Reinforcement Learning. [pdf]
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• Provable Benefit of Multitask Representation Learning in Reinforcement Learning. [pdf]
  • Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang. NeurIPS 2022.
• Modeling Human Exploration Through Resource-Rational Reinforcement Learning. [pdf]
  • Marcel Binz, Eric Schulz. NeurIPS 2022.
• Factored Adaptation for Non-Stationary Reinforcement Learning. [pdf]
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• Robust Reinforcement Learning using Offline Data. [pdf]
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• Model-based Lifelong Reinforcement Learning with Bayesian Exploration. [pdf]
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• Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning. [pdf]
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• Efficient Risk-Averse Reinforcement Learning. [pdf]
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• VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning. [pdf]
  • Che Wang, Xufang Luo, Keith W. Ross, Dongsheng Li. NeurIPS 2022.
• Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning. [pdf]
  • Byungchan Ko, Jungseul Ok. NeurIPS 2022.
• Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems. [pdf]
  • Shicheng Liu, Minghui Zhu. NeurIPS 2022.
• Universally Expressive Communication in Multi-Agent Reinforcement Learning. [pdf]
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• A Boosting Approach to Reinforcement Learning. [pdf]
  • Nataly Brukhim, Elad Hazan, Karan Singh. NeurIPS 2022.
• Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments. [pdf]
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• Explainable Reinforcement Learning via Model Transforms. [pdf]
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• Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. [pdf]
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• Unsupervised Reinforcement Learning with Contrastive Intrinsic Control. [pdf]
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• Bootstrapped Transformer for Offline Reinforcement Learning. [pdf]
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• Rethinking Value Function Learning for Generalization in Reinforcement Learning. [pdf]
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• Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing. [pdf]
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• Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework. [pdf]
  • Henger Li, Xiaolin Sun, Zizhan Zheng. NeurIPS 2022.
• Recursive Reinforcement Learning. [pdf]
  • Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak. NeurIPS 2022.
• Contrastive Learning as Goal-Conditioned Reinforcement Learning. [pdf]
  • Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov. NeurIPS 2022.
• Reinforcement Learning with a Terminator. [pdf]
  • Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal. NeurIPS 2022.
• Reinforcement Learning with Logarithmic Regret and Policy Switches. [pdf]
  • Grigoris Velegkas, Zhuoran Yang, Amin Karbasi. NeurIPS 2022.
• Regret Bounds for Risk-Sensitive Reinforcement Learning. [pdf]
  • Osbert Bastani, Yecheng Jason Ma, Estelle Shen, Wanqiao Xu. NeurIPS 2022.
• Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. [pdf]
  • Dongruo Zhou, Quanquan Gu. NeurIPS 2022.
• Object-Category Aware Reinforcement Learning. [pdf]
  • Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen. NeurIPS 2022.
• When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning. [pdf]
  • Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, Xianyuan Zhan. NeurIPS 2022.
• Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning. [pdf]
  • Jianda Chen, Sinno Jialin Pan. NeurIPS 2022.
• LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning. [pdf]
  • Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Jianhao Wang, Alex Yuan Gao, Wenzhe Li, Liang Bin, Chelsea Finn, Chongjie Zhang. NeurIPS 2022.
• Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning. [pdf]
  • Rujie Zhong, Duohan Zhang, Lukas Schäfer, Stefano V. Albrecht, Josiah Hanna. NeurIPS 2022.
• Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning. [pdf]
  • Minjong Yoo, Sangwoo Cho, Honguk Woo. NeurIPS 2022.
• ASPiRe: Adaptive Skill Priors for Reinforcement Learning. [pdf]
  • Mengda Xu, Manuela Veloso, Shuran Song. NeurIPS 2022.
• DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning. [pdf]
  • Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar. NeurIPS 2022.