Allenpandas / 2021-Reinforcement-Learning-Conferences-Papers

The proceedings of top conference in 2021 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.

2021-Reinforcement-Learning-Conferences-Papers

The proceedings of top conference in 2021 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'2021
• AAMAS'2021
• ICLR'2021
• ICML'2021
• ICRA'2021
• IJCAI'2021
• NeurIPS'2021

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'2021)
• International Conference on Autonomous Agents and Multiagent Systems (AAMAS'2021)
• International Conference on Learning Representations (ICLR'2021)
• International Conference on Machine Learning (ICML'2021)
• International Conference on Robotics and Automation (ICRA'2021)
• International Joint Conference on Artificial Intelligence (IJCAI'2021)
• Annual Conference on Neural Information Processing Systems (NeurIPS'2021)

AAAI Conference on Artificial Intelligence

• Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery. [pdf]
  • Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, Sarath Chandar. AAAI 2021.
• Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service. [pdf]
  • Majid Raeis, Ali Tizghadam, Alberto Leon-Garcia. AAAI 2021.
• Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agent. [pdf]
  • Peter Schaldenbrand, Jean Oh. AAAI 2021.
• DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. [pdf]
  • Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, Lei Xu. AAAI 2021.
• Online 3D Bin Packing with Constrained Deep Reinforcement Learning. [pdf]
  • Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, Kai Xu. AAAI 2021.
• DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems. [pdf]
  • Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Jiliang Tang, Hui Liu. AAAI 2021.
• Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images. [pdf]
  • Hak Gu Kim, Minho Park, Sangmin Lee, Seongyeop Kim, Yong Man Ro. AAAI 2021.
• FontRL: Chinese Font Synthesis via Deep Reinforcement Learning. [pdf]
  • Yitian Liu, Zhouhui Lian. AAAI 2021.
• Visual Tracking via Hierarchical Deep Reinforcement Learning. [pdf]
  • Dawei Zhang, Zhonglong Zheng, Riheng Jia, Minglu Li. AAAI 2021.
• Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization. [pdf]
  • Quentin Cappart, Thierry Moisan, Louis-Martin Rousseau, Isabeau Prémont-Schwarz, André A. Ciré. AAAI 2021.
• Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation. [pdf]
  • Kai Wang, Zhene Zou, Qilin Deng, Jianrong Tao, Runze Wu, Changjie Fan, Liang Chen, Peng Cui. AAAI 2021.
• A General Offline Reinforcement Learning Framework for Interactive Recommendation. [pdf]
  • Teng Xiao, Donglin Wang. AAAI 2021.
• Hierarchical Reinforcement Learning for Integrated Recommendation. [pdf]
  • Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, Leyu Lin. AAAI 2021.
• Encoding Human Domain Knowledge to Warm Start Reinforcement Learning. [pdf]
  • Andrew Silva, Matthew C. Gombolay. AAAI 2021.
• Reinforcement Learning of Sequential Price Mechanisms. [pdf]
  • Gianluca Brero, Alon Eden, Matthias Gerstgrasser, David C. Parkes, Duncan Rheingans-Yoo. AAAI 2021.
• A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving. [pdf]
  • Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue. AAAI 2021.
• The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. [pdf]
  • Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver. AAAI 2021.
• Reinforcement Learning with Trajectory Feedback. [pdf]
  • Yonathan Efroni, Nadav Merlis, Shie Mannor. AAAI 2021.
• Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning. [pdf]
  • Haotian Fu, Hongyao Tang, Jianye Hao, Chen Chen, Xidong Feng, Dong Li, Wulong Liu. AAAI 2021.
• DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning. [pdf]
  • Mohammadhosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate, Tom Melham, Daniel Kroening. AAAI 2021.
• Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs. [pdf]
  • Aria HasanzadeZonuzy, Archana Bura, Dileep M. Kalathil, Srinivas Shakkottai. AAAI 2021.
• Reinforcement Learning Based Multi-Agent Resilient Control: From Deep Neural Networks to an Adaptive Law. [pdf]
  • Jian Hou, Fangyuan Wang, Lili Wang, Zhiyong Chen. AAAI 2021.
• Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning. [pdf]
  • Wenzhen Huang, Qiyue Yin, Junge Zhang, Kaiqi Huang. AAAI 2021.
• Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks. [pdf]
  • Yuqian Jiang, Suda Bharadwaj, Bo Wu, Rishi Shah, Ufuk Topcu, Peter Stone. AAAI 2021.
• Metrics and Continuity in Reinforcement Learning. [pdf]
  • Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro. AAAI 2021.
• Lipschitz Lifelong Reinforcement Learning. [pdf]
  • Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman. AAAI 2021.
• Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning. [pdf]
  • Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Basar, Lior Horesh. AAAI 2021.
• Exact Reduction of Huge Action Spaces in General Reinforcement Learning. [pdf]
  • Sultan Javed Majeed, Marcus Hutter. AAAI 2021.
• Scheduling of Time-Varying Workloads Using Reinforcement Learning. [pdf]
  • Shanka Subhra Mondal, Nikhil Sheoran, Subrata Mitra. AAAI 2021.
• Advice-Guided Reinforcement Learning in a non-Markovian Environment. [pdf]
  • Daniel Neider, Jean-Raphaël Gaglione, Ivan Gavran, Ufuk Topcu, Bo Wu, Zhe Xu. AAAI 2021.
• Distributional Reinforcement Learning via Moment Matching. [pdf]
  • Thanh Nguyen-Tang, Sunil Gupta, Svetha Venkatesh. AAAI 2021.
• Inverse Reinforcement Learning From Like-Minded Teachers. [pdf]
  • Ritesh Noothigattu, Tom Yan, Ariel D. Procaccia. AAAI 2021.
• Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation. [pdf]
  • Bo Pang, Zhong-Ping Jiang. AAAI 2021.
• Visual Transfer For Reinforcement Learning Via Wasserstein Domain Confusion. [pdf]
  • Josh Roy, George Dimitri Konidaris. AAAI 2021.
• Inverse Reinforcement Learning with Explicit Policy Estimates. [pdf]
  • Navyata Sanghvi, Shinnosuke Usami, Mohit Sharma, Joachim Groeger, Kris Kitani. AAAI 2021.
• Self-Supervised Attention-Aware Reinforcement Learning. [pdf]
  • Haiping Wu, Khimya Khetarpal, Doina Precup. AAAI 2021.
• Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation. [pdf]
  • Jinwei Xing, Takashi Nagata, Kexin Chen, Xinyun Zou, Emre Neftci, Jeffrey L. Krichmar. AAAI 2021.
• Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling. [pdf]
  • Huaqing Xiong, Tengyu Xu, Yingbin Liang, Wei Zhang. AAAI 2021.
• WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning. [pdf]
  • Qisong Yang, Thiago D. Simão, Simon H. Tindemans, Matthijs T. J. Spaan. AAAI 2021.
• Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. [pdf]
  • Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus. AAAI 2021.
• Sequential Generative Exploration Model for Partially Observable Reinforcement Learning. [pdf]
  • Haiyan Yin, Jianda Chen, Sinno Jialin Pan, Sebastian Tschiatschek. AAAI 2021.
• Sample Efficient Reinforcement Learning with REINFORCE. [pdf]
  • Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen P. Boyd. AAAI 2021.
• Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning. [pdf]
  • Shangtong Zhang, Bo Liu, Shimon Whiteson. AAAI 2021.
• Inverse Reinforcement Learning with Natural Language Goals. [pdf]
  • Li Zhou, Kevin Small. AAAI 2021.
• Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition. [pdf]
  • Thomy Phan, Lenz Belzner, Thomas Gabor, Andreas Sedlmeier, Fabian Ritz, Claudia Linnhoff-Popien. AAAI 2021.
• Coordination Between Individual Agents in Multi-Agent Reinforcement Learning. [pdf]
  • Yang Zhang, Qingyu Yang, Dou An, Chengwei Zhang. AAAI 2021.
• GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling. [pdf]
  • Rohan Chitnis, Tom Silver, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Tomás Lozano-Pérez. AAAI 2021.
• Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning. [pdf]
  • Andrea Micheli, Alessandro Valentini. AAAI 2021.
• Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem. [pdf]
  • Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-Min Li. AAAI 2021.
• Adaptive Prior-Dependent Correction Enhanced Reinforcement Learning for Natural Language Generation. [pdf]
  • Wei Cheng, Ziyan Luo, Qiyue Yin. AAAI 2021.
• Reinforcement Learning-based Product Delivery Frequency Control. [pdf]
  • Yang Liu, Zhengxing Chen, Kittipat Virochsiri, Juan Wang, Jiahao Wu, Feng Liang. AAAI 2021.
• Multi-agent Reinforcement Learning for Decentralized Coalition Formation Games. [pdf]
  • Kshitija Taywade. AAAI 2021.
• Robotic Manipulation with Reinforcement Learning, State Representation Learning, and Imitation Learning (Student Abstract). [pdf]
  • Hanxiao Chen. AAAI 2021.
• Evaluating Meta-Reinforcement Learning through a HVAC Control Benchmark (Student Abstract). [pdf]
  • Yashvir S. Grewal, Frits de Nijs, Sarah Goodwin. AAAI 2021.
• Leveraging on Deep Reinforcement Learning for Autonomous Safe Decision-Making in Highway On-ramp Merging (Student Abstract). [pdf]
  • Zine El Abidine Kherroubi, Samir Aknine, Rebiha Bacha. AAAI 2021.
• Deep Reinforcement Learning for a Dictionary Based Compression Schema (Student Abstract). [pdf]
  • Keren Nivasch, Dana Shapira, Amos Azaria. AAAI 2021.
• Solving JumpIN' Using Zero-Dependency Reinforcement Learning (Student Abstract). [pdf]
  • Rachel Ostic, Oliver Benning, Patrick Boily. AAAI 2021.
• Enhancing Context-Based Meta-Reinforcement Learning Algorithms via An Efficient Task Encoder (Student Abstract). [pdf]
  • Feng Xu, Shengyi Jiang, Hao Yin, Zongzhang Zhang, Yang Yu, Ming Li, Dong Li, Wulong Liu. AAAI 2021.
• State-Wise Adaptive Discounting from Experience (SADE): A Novel Discounting Scheme for Reinforcement Learning (Student Abstract). [pdf]
  • Milan Zinzuvadiya, Vahid Behzadan. AAAI 2021.
• Exploration of Unknown Environments Using Deep Reinforcement Learning. [pdf]
  • Joseph McCalmon. AAAI 2021.
• EasyRL: A Simple and Extensible Reinforcement Learning Framework. [pdf]
  • Neil Hulbert, Sam Spillers, Brandon Francis, James Haines-Temons, Ken Gil Romero, Benjamin De Jager, Sam Wong, Kevin Flora, Bowei Huang, Athirai A. Irissappane. AAAI 2021.

International Conference on Autonomous Agents and Multiagent Systems

• Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection. [pdf]
  • Lucas Nunes Alegre, Ana L. C. Bazzan, Bruno C. da Silva. AAMAS 2021.
• Cooperation and Reputation Dynamics with Reinforcement Learning. [pdf]
  • Nicolas Anastassacos, Julian García, Stephen Hailes, Mirco Musolesi. AAMAS 2021.
• Temporal Watermarks for Deep Reinforcement Learning Models. [pdf]
  • Kangjie Chen, Shangwei Guo, Tianwei Zhang, Shuxin Li, Yang Liu. AAMAS 2021.
• Safe Multi-Agent Reinforcement Learning via Shielding. [pdf]
  • Ingy Elsayed-Aly, Suda Bharadwaj, Christopher Amato, Rüdiger Ehlers, Ufuk Topcu, Lu Feng. AAMAS 2021.
• Partially Observable Mean Field Reinforcement Learning. [pdf]
  • Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart. AAMAS 2021.
• Action Selection for Composable Modular Deep Reinforcement Learning. [pdf]
  • Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar. AAMAS 2021.
• Multi-Agent Reinforcement Learning with Temporal Logic Specifications. [pdf]
  • Lewis Hammond, Alessandro Abate, Julian Gutierrez, Michael J. Wooldridge. AAMAS 2021.
• Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards. [pdf]
  • Keyang He, Bikramjit Banerjee, Prashant Doshi. AAMAS 2021.
• Action Advising with Advice Imitation in Deep Reinforcement Learning. [pdf]
  • Ercument Ilhan, Jeremy Gow, Diego Perez Liebana. AAMAS 2021.
• Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning. [pdf]
  • Zhengyao Jiang, Pasquale Minervini, Minqi Jiang, Tim Rocktäschel. AAMAS 2021.
• Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning. [pdf]
  • Sheng Li, Jayesh K. Gupta, Peter Morales, Ross E. Allen, Mykel J. Kochenderfer. AAMAS 2021.
• Parallel Curriculum Experience Replay in Distributed Reinforcement Learning. [pdf]
  • Yuyu Li, Jianmin Ji. AAMAS 2021.
• Deceptive Reinforcement Learning for Privacy-Preserving Planning. [pdf]
  • Zhengshang Liu, Yue Yang, Tim Miller, Peta Masters. AAMAS 2021.
• Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning. [pdf]
  • Xueguang Lyu, Yuchen Xiao, Brett Daley, Christopher Amato. AAMAS 2021.
• Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Xiaoteng Ma, Yiqin Yang, Chenghao Li, Yiwen Lu, Qianchuan Zhao, Jun Yang. AAMAS 2021.
• To hold or not to hold? - Reducing Passenger Missed Connections in Airlines using Reinforcement Learning. [pdf]
  • Tejasvi Malladi, Karpagam Murugappan, Depak Sudarsanam, Ramasubramanian Suriyanarayanan, Arunchandar Vasan. AAMAS 2021.
• Reward Machines for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Cyrus Neary, Zhe Xu, Bo Wu, Ufuk Topcu. AAMAS 2021.
• Active Screening for Recurrent Diseases: A Reinforcement Learning Approach. [pdf]
  • Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe. AAMAS 2021.
• Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning. [pdf]
  • Heechang Ryu, Hayong Shin, Jinkyoo Park. AAMAS 2021.
• SEERL: Sample Efficient Ensemble Reinforcement Learning. [pdf]
  • Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul. AAMAS 2021.
• SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning. [pdf]
  • Vasanth Sarathy, Daniel Kasenberg, Shivam Goel, Jivko Sinapov, Matthias Scheutz. AAMAS 2021.
• An Autonomous Negotiating Agent Framework with Reinforcement Learning based Strategies and Adaptive Strategy Switching Mechanism. [pdf]
  • Ayan Sengupta, Yasser Mohammad, Shinji Nakadai. AAMAS 2021.
• AlwaysSafe: Reinforcement Learning without Safety Constraint Violations during Training. [pdf]
  • Thiago D. Simão, Nils Jansen, Matthijs T. J. Spaan. AAMAS 2021.
• No More Hand-Tuning Rewards: Masked Constrained Policy Optimization for Safe Reinforcement Learning. [pdf]
  • Stef Van Havermaet, Yara Khaluf, Pieter Simoens. AAMAS 2021.
• Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty. [pdf]
  • Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe. AAMAS 2021.
• Transferable Environment Poisoning: Training-time Attack on Reinforcement Learning. [pdf]
  • Hang Xu, Rundong Wang, Lev Raizman, Zinovi Rabinovich. AAMAS 2021.
• Learning to Cooperate with Unseen Agents Through Meta-Reinforcement Learning. [pdf]
  • Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul. AAMAS 2021.
• Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning. [pdf]
  • Brett Daley, Cameron Hickert, Christopher Amato. AAMAS 2021.
• Towards Decentralized Social Reinforcement Learning via Ego-Network Extrapolation. [pdf]
  • Mahak Goindani, Jennifer Neville. AAMAS 2021.
• Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning. [pdf]
  • Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion. AAMAS 2021.
• Solving 3D Bin Packing Problem via Multimodal Deep Reinforcement Learning. [pdf]
  • Yuan Jiang, Zhiguang Cao, Jie Zhang. AAMAS 2021.
• SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning. [pdf]
  • Somjit Nath, Richa Verma, Abhik Ray, Harshad Khadilkar. AAMAS 2021.
• Tunable Behaviours in Sequential Social Dilemmas using Multi-Objective Reinforcement Learning. [pdf]
  • David O'Callaghan, Patrick Mannion. AAMAS 2021.
• Attention Actor-Critic Algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning. [pdf]
  • P. Parnika, Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda, Shalabh Bhatnagar. AAMAS 2021.
• Approximate Difference Rewards for Scalable Multigent Reinforcement Learning. [pdf]
  • Arambam James Singh, Akshat Kumar, Hoong Chuin Lau. AAMAS 2021.
• A Distributional Perspective on Value Function Factorization Methods for Multi-Agent Reinforcement Learning. [pdf]
  • Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee. AAMAS 2021.
• Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. [pdf]
  • Luisa M. Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann. AAMAS 2021.
• Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework. [pdf]
  • Guangliang Li, Hamdi Dibeklioglu, Shimon Whiteson, Hayley Hung. AAMAS 2021.
• Symbolic Reinforcement Learning for Safe RAN Control. [pdf]
  • Alexandros Nikou, Anusha Mujumdar, Marin Orlic, Aneta Vulgarakis Feljan. AAMAS 2021.
• Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior. [pdf]
  • Hossein Haeri. AAMAS 2021.
• Improving Sample-based Reinforcement Learning through Complex Non-parametric Distributions. [pdf]
  • Shi Yuan Tang. AAMAS 2021.

International Conference on Learning Representations

• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. [pdf]
  • Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine. ICLR 2021.
• Learning Invariant Representations for Reinforcement Learning without Reconstruction. [pdf]
  • Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine. ICLR 2021.
• SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. [pdf]
  • Glen Berseth, Daniel Geng, Coline Manon Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine. ICLR 2021.
• Evolving Reinforcement Learning Algorithms. [pdf]
  • John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V. Le, Sergey Levine, Honglak Lee, Aleksandra Faust. ICLR 2021.
• Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions. [pdf]
  • Zhengxian Lin, Kin-Ho Lam, Alan Fern. ICLR 2021.
• Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. [pdf]
  • Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare. ICLR 2021.
• Data-Efficient Reinforcement Learning with Self-Predictive Representations. [pdf]
  • Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman. ICLR 2021.
• Regularized Inverse Reinforcement Learning. [pdf]
  • Wonseok Jeon, Chen-Yang Su, Paul Barde, Thang Doan, Derek Nowrouzezahrai, Joelle Pineau. ICLR 2021.
• DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs. [pdf]
  • Aayam Kumar Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern. ICLR 2021.
• Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels. [pdf]
  • Denis Yarats, Ilya Kostrikov, Rob Fergus. ICLR 2021.
• Self-supervised Visual Reinforcement Learning with Object-centric Representations. [pdf]
  • Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius. ICLR 2021.
• Optimism in Reinforcement Learning with Generalized Linear Function Approximation. [pdf]
  • Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy. ICLR 2021.
• Control-Aware Representations for Model-based Reinforcement Learning. [pdf]
  • Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh. ICLR 2021.
• Robust Reinforcement Learning on State Observations with Learned Optimal Adversary. [pdf]
  • Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh. ICLR 2021.
• Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. [pdf]
  • Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar. ICLR 2021.
• Representation Balancing Offline Model-based Reinforcement Learning. [pdf]
  • Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim. ICLR 2021.
• Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning. [pdf]
  • Valerie Chen, Abhinav Gupta, Kenneth Marino. ICLR 2021.
• Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation. [pdf]
  • Emilio Parisotto, Ruslan Salakhutdinov. ICLR 2021.
• Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL. [pdf]
  • Xiaoyu Chen, Jiachen Hu, Lihong Li, Liwei Wang. ICLR 2021.
• Hierarchical Reinforcement Learning by Discovering Intrinsic Options. [pdf]
  • Jesse Zhang, Haonan Yu, Wei Xu. ICLR 2021.
• Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. [pdf]
  • Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov. ICLR 2021.
• Blending MPC & Value Function Approximation for Efficient Reinforcement Learning. [pdf]
  • Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots. ICLR 2021.
• Efficient Wasserstein Natural Gradients for Reinforcement Learning. [pdf]
  • Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton. ICLR 2021.
• Differentiable Trust Region Layers for Deep Reinforcement Learning. [pdf]
  • Fabian Otto, Philipp Becker, Ngo Anh Vien, Hanna Carolin Maria Ziesche, Gerhard Neumann. ICLR 2021.
• Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning. [pdf]
  • Enrico Marchesini, Davide Corsi, Alessandro Farinelli. ICLR 2021.
• Reinforcement Learning with Random Delays. [pdf]
  • Yann Bouteiller, Simon Ramstedt, Giovanni Beltrame, Christopher J. Pal, Jonathan Binas. ICLR 2021.
• Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. [pdf]
  • Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu. ICLR 2021.
• Risk-Averse Offline Reinforcement Learning. [pdf]
  • Núria Armengol Urpí, Sebastian Curi, Andreas Krause. ICLR 2021.
• Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. [pdf]
  • Dipendra Misra, Qinghua Liu, Chi Jin, John Langford. ICLR 2021.
• Return-Based Contrastive Representation Learning for Reinforcement Learning. [pdf]
  • Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Jian Li, Nenghai Yu, Tie-Yan Liu. ICLR 2021.
• Scalable Bayesian Inverse Reinforcement Learning. [pdf]
  • Alex James Chan, Mihaela van der Schaar. ICLR 2021.
• Transient Non-stationarity and Generalisation in Deep Reinforcement Learning. [pdf]
  • Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson. ICLR 2021.
• Sample-Efficient Automated Deep Reinforcement Learning. [pdf]
  • Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter. ICLR 2021.
• OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. [pdf]
  • Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum. ICLR 2021.
• Batch Reinforcement Learning Through Continuation Method. [pdf]
  • Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed H. Chi, Honglak Lee, Minmin Chen. ICLR 2021.
• Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. [pdf]
  • Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine. ICLR 2021.
• Large Batch Simulation for Deep Reinforcement Learning. [pdf]
  • Brennan Shacklett, Erik Wijmans, Aleksei Petrenko, Manolis Savva, Dhruv Batra, Vladlen Koltun, Kayvon Fatahalian. ICLR 2021.
• Adapting to Reward Progressivity via Spectral Reinforcement Learning. [pdf]
  • Michael Dann, John Thangarajah. ICLR 2021.
• FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization. [pdf]
  • Lanqing Li, Rui Yang, Dijun Luo. ICLR 2021.
• Communication in Multi-Agent Reinforcement Learning: Intention Sharing. [pdf]
  • Woojun Kim, Jongeui Park, Youngchul Sung. ICLR 2021.
• Solving Compositional Reinforcement Learning Problems via Task Reduction. [pdf]
  • Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, Yi Wu. ICLR 2021.

International Conference on Machine Learning

• Safe Reinforcement Learning with Linear Function Approximation. [pdf]
  • Sanae Amani, Christos Thrampoulidis, Lin Yang. ICML 2021.
• Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees. [pdf]
  • Kishan Panaganti Badrinath, Dileep Kalathil. ICML 2021.
• Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision. [pdf]
  • Johan Björck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger. ICML 2021.
• Reinforcement Learning of Implicit and Explicit Control Flow Instructions. [pdf]
  • Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh. ICML 2021.
• Learning Routines for Effective Off-Policy Reinforcement Learning. [pdf]
  • Edoardo Cetin, Oya Çeliktutan. ICML 2021.
• Goal-Conditioned Reinforcement Learning with Imagined Subgoals. [pdf]
  • Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev. ICML 2021.
• Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. [pdf]
  • Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths. ICML 2021.
• Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning. [pdf]
  • Henry Charlesworth, Giovanni Montana. ICML 2021.
• Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. [pdf]
  • Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine. ICML 2021.
• Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. [pdf]
  • Yifang Chen, Simon S. Du, Kevin Jamieson. ICML 2021.
• Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning. [pdf]
  • Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu. ICML 2021.
• Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing. [pdf]
  • Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht. ICML 2021.
• Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning. [pdf]
  • Sebastian Curi, Ilija Bogunovic, Andreas Krause. ICML 2021.
• Offline Reinforcement Learning with Pseudometric Learning. [pdf]
  • Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist. ICML 2021.
• Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation. [pdf]
  • Christopher R. Dance, Julien Perez, Théo Cachet. ICML 2021.
• SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning. [pdf]
  • Lokesh Chandra Das, Myounggyu Won. ICML 2021.
• Kernel-Based Reinforcement Learning: A Finite-Time Analysis. [pdf]
  • Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko. ICML 2021.
• Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning. [pdf]
  • Yaqi Duan, Chi Jin, Zhiyuan Li. ICML 2021.
• Reinforcement Learning Under Moral Uncertainty. [pdf]
  • Adrien Ecoffet, Joel Lehman. ICML 2021.
• Self-Paced Context Evaluation for Contextual Reinforcement Learning. [pdf]
  • Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer. ICML 2021.
• Model-based Reinforcement Learning for Continuous Control with Posterior Sampling. [pdf]
  • Ying Fan, Yifei Ming. ICML 2021.
• Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach. [pdf]
  • Yingjie Fei, Zhuoran Yang, Zhaoran Wang. ICML 2021.
• PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. [pdf]
  • Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar. ICML 2021.
• A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation. [pdf]
  • Scott Fujimoto, David Meger, Doina Precup. ICML 2021.
• Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. [pdf]
  • Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu. ICML 2021.
• Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective. [pdf]
  • Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu. ICML 2021.
• Detecting Rewards Deterioration in Episodic Reinforcement Learning. [pdf]
  • Ido Greenberg, Shie Mannor. ICML 2021.
• UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. [pdf]
  • Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson. ICML 2021.
• Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning. [pdf]
  • Austin W. Hanjie, Victor Zhong, Karthik Narasimhan. ICML 2021.
• Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. [pdf]
  • Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang. ICML 2021.
• Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. [pdf]
  • Jiafan He, Dongruo Zhou, Quanquan Gu. ICML 2021.
• Generalizable Episodic Memory for Deep Reinforcement Learning. [pdf]
  • Hao Hu, Jianing Ye, Guangxiang Zhu, Zhizhou Ren, Chongjie Zhang. ICML 2021.
• Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. [pdf]
  • Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha. ICML 2021.
• Randomized Exploration in Reinforcement Learning with General Value Function Approximation. [pdf]
  • Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang. ICML 2021.
• Emphatic Algorithms for Deep Reinforcement Learning. [pdf]
  • Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt. ICML 2021.
• Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations. [pdf]
  • Angeliki Kamoutsi, Goran Banjac, John Lygeros. ICML 2021.
• Reward Identification in Inverse Reinforcement Learning. [pdf]
  • Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon. ICML 2021.
• A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. [pdf]
  • Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How. ICML 2021.
• A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning. [pdf]
  • Abi Komanduru, Jean Honorio. ICML 2021.
• High Confidence Generalization for Reinforcement Learning. [pdf]
  • James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas. ICML 2021.
• Offline Reinforcement Learning with Fisher Divergence Critic Regularization. [pdf]
  • Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum. ICML 2021.
• Revisiting Peng's Q(λ) for Modern Reinforcement Learning. [pdf]
  • Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel. ICML 2021.
• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning. [pdf]
  • Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel. ICML 2021.
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training. [pdf]
  • Kimin Lee, Laura M. Smith, Pieter Abbeel. ICML 2021.
• Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. [pdf]
  • Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel. ICML 2021.
• MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. [pdf]
  • Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H. Pong, Aurick Zhou, Justin Yu, Sergey Levine. ICML 2021.
• Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning. [pdf]
  • Tung-Che Liang, Jin Zhou, Yun-Sheng Chan, Tsung-Yi Ho, Krishnendu Chakrabarty, Cy Lee. ICML 2021.
• Cooperative Exploration for Multi-Agent Deep Reinforcement Learning. [pdf]
  • Iou-Jen Liu, Unnat Jain, Raymond A. Yeh, Alexander G. Schwing. ICML 2021.
• Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. [pdf]
  • Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar. ICML 2021.
• Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. [pdf]
  • Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn. ICML 2021.
• A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. [pdf]
  • Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin. ICML 2021.
• Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. [pdf]
  • Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar. ICML 2021.
• Inverse Constrained Reinforcement Learning. [pdf]
  • Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed. ICML 2021.
• Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. [pdf]
  • Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li. ICML 2021.
• Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs. [pdf]
  • Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar. ICML 2021.
• Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. [pdf]
  • Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik. ICML 2021.
• Counterfactual Credit Assignment in Model-Free Reinforcement Learning. [pdf]
  • Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Rémi Munos. ICML 2021.
• Offline Meta-Reinforcement Learning with Advantage Weighting. [pdf]
  • Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn. ICML 2021.
• Emergent Social Learning via Multi-agent Reinforcement Learning. [pdf]
  • Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques. ICML 2021.
• Density Constrained Reinforcement Learning. [pdf]
  • Zengyi Qin, Yuxiao Chen, Chuchu Fan. ICML 2021.
• Decoupling Value and Policy for Generalization in Reinforcement Learning. [pdf]
  • Roberta Raileanu, Rob Fergus. ICML 2021.
• Model-Based Reinforcement Learning via Latent-Space Collocation. [pdf]
  • Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine. ICML 2021.
• Recomposing the Reinforcement Learning Building Blocks with Hypernetworks. [pdf]
  • Elad Sarafian, Shai Keynan, Sarit Kraus. ICML 2021.
• RRL: Resnet as representation for Reinforcement Learning. [pdf]
  • Rutav M. Shah, Vikash Kumar. ICML 2021.
• Structured World Belief for Reinforcement Learning in POMDP. [pdf]
  • Gautam Singh, Skand Vishwanath Peri, Junghyun Kim, Hyunseok Kim, Sungjin Ahn. ICML 2021.
• Multi-Task Reinforcement Learning with Context-based Representations. [pdf]
  • Shagun Sodhani, Amy Zhang, Joelle Pineau. ICML 2021.
• Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks. [pdf]
  • Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm van Seijen, Mehdi Fatemi, Honglak Lee. ICML 2021.
• PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration. [pdf]
  • Yuda Song, Wen Sun. ICML 2021.
• Decoupling Representation Learning from Reinforcement Learning. [pdf]
  • Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin. ICML 2021.
• Reinforcement Learning for Cost-Aware Markov Decision Processes. [pdf]
  • Wesley Suttle, Kaiqing Zhang, Zhuoran Yang, Ji Liu, David N. Kraemer. ICML 2021.
• REPAINT: Knowledge Transfer in Deep Reinforcement Learning. [pdf]
  • Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya. ICML 2021.
• Safe Reinforcement Learning Using Advantage-Based Intervention. [pdf]
  • Nolan Wagener, Byron Boots, Ching-An Cheng. ICML 2021.
• Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing. [pdf]
  • Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng. ICML 2021.
• On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP. [pdf]
  • Tianhao Wu, Yunchang Yang, Simon S. Du, Liwei Wang. ICML 2021.
• Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning. [pdf]
  • Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh. ICML 2021.
• Deep Reinforcement Learning amidst Continual Structured Non-Stationarity. [pdf]
  • Annie Xie, James Harrison, Chelsea Finn. ICML 2021.
• CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee. [pdf]
  • Tengyu Xu, Yingbin Liang, Guanghui Lan. ICML 2021.
• Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies. [pdf]
  • Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge. ICML 2021.
• Reinforcement Learning with Prototypical Representations. [pdf]
  • Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto. ICML 2021.
• Continuous-time Model-based Reinforcement Learning. [pdf]
  • Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki. ICML 2021.
• Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL. [pdf]
  • Andrea Zanette. ICML 2021.
• DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning. [pdf]
  • Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu. ICML 2021.
• Near Optimal Reward-Free Reinforcement Learning. [pdf]
  • Zihan Zhang, Simon S. Du, Xiangyang Ji. ICML 2021.
• FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning. [pdf]
  • Tianhao Zhang, Yueheng Li, Chen Wang, Guangming Xie, Zongqing Lu. ICML 2021.
• On-Policy Deep Reinforcement Learning for the Average-Reward Criterion. [pdf]
  • Yiming Zhang, Keith W. Ross. ICML 2021.
• MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration. [pdf]
  • Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan, Chongjie Zhang. ICML 2021.
• Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity. [pdf]
  • Zihan Zhang, Yuan Zhou, Xiangyang Ji. ICML 2021.
• Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. [pdf]
  • Dongruo Zhou, Jiafan He, Quanquan Gu. ICML 2021.
• Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Matthieu Zimmer, Claire Glanois, Umer Siddique, Paul Weng. ICML 2021.
• Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. [pdf]
  • Luisa M. Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson. ICML 2021.

International Conference on Robotics and Automation

• Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot. [pdf]
  • Yunlei Shi, Zhaopeng Chen, Hongxu Liu, Sebastian Riedel, Chunhui Gao, Qian Feng, Jun Deng, Jianwei Zhang. ICRA 2021.
• Model-based Reinforcement Learning with Provable Safety Guarantees via Control Barrier Functions. [pdf]
  • Hongchao Zhang, Zhouchi Li, Andrew Clark. ICRA 2021.
• Continual Model-Based Reinforcement Learning with Hypernetworks. [pdf]
  • Yizhou Huang, Kevin Xie, Homanga Bharadhwaj, Florian Shkurti. ICRA 2021.
• Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction. [pdf]
  • Mingyu Cai, Shaoping Xiao, Baoluo Li, Zhiliang Li, Zhen Kan. ICRA 2021.
• Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning. [pdf]
  • Pin Wang, Dapeng Liu, Jiayu Chen, Hanhan Li, Ching-Yao Chan. ICRA 2021.
• Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition. [pdf]
  • Ricardo B. Grando, Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling, Nicolas P. Bortoluzzi, Pedro M. Pinheiro, Armando Alves Neto, Paulo Lilles Jorge Drews Junior. ICRA 2021.
• Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning. [pdf]
  • Claudia Pérez-D'Arpino, Can Liu, Patrick Goebel, Roberto Martín-Martín, Silvio Savarese. ICRA 2021.
• Reinforcement Learning Control of A Novel Magnetic Actuated Flexible-joint Robotic Camera System for Single Incision Laparoscopic Surgery. [pdf]
  • Dong Xu, Yuanlin Zhang, Wenshuai Tan, Hongxing Wei. ICRA 2021.
• Deep Reinforcement Learning for Concentric Tube Robot Control with a Goal-Based Curriculum. [pdf]
  • Keshav Iyengar, Danail Stoyanov. ICRA 2021.
• Regularizing Action Policies for Smooth Control with Reinforcement Learning. [pdf]
  • Siddharth Mysore, Bassel Mabsout, Renato Mancuso, Kate Saenko. ICRA 2021.
• Deep Reinforcement Learning for Active Target Tracking. [pdf]
  • Heejin Jeong, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas. ICRA 2021.
• Reaching Pruning Locations in a Vine Using a Deep Reinforcement Learning Policy. [pdf]
  • Francisco Yandún, Tanvir Parhar, Abhisesh Silwal, David Clifford, Zhiqiang Yuan, Gabriella Levine, Sergey Yaroshenko, George Kantor. ICRA 2021.
• A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning. [pdf]
  • Jinning Li, Liting Sun, Jianyu Chen, Masayoshi Tomizuka, Wei Zhan. ICRA 2021.
• Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning [pdf]
  • Zhiqian Qiao, Jeff Schneider, John M. Dolan. ICRA 2021.
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots. [pdf]
  • Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath. ICRA 2021.
• SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning. [pdf]
  • Yifeng Jiang, Tingnan Zhang, Daniel Ho, Yunfei Bai, C. Karen Liu, Sergey Levine, Jie Tan. ICRA 2021.
• DeepWalk: Omnidirectional Bipedal Gait by Deep Reinforcement Learning. [pdf]
  • Diego Rodriguez, Sven Behnke. ICRA 2021.
• Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning. [pdf]
  • Shuijing Liu, Peixin Chang, Weihang Liang, Neeloy Chakraborty, Katherine Driggs Campbell. ICRA 2021.
• Mesh Based Analysis of Low Fractal Dimension Reinforcement Learning Policies. [pdf]
  • Sean Gillen, Katie Byl. ICRA 2021.
• Evolvable Motion-planning Method using Deep Reinforcement Learning. [pdf]
  • Kaichiro Nishi, Nobuaki Nakasu. ICRA 2021.
• Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning. [pdf]
  • Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters. ICRA 2021.
• Sample-efficient Reinforcement Learning in Robotic Table Tennis. [pdf]
  • Jonas Tebbe, Lukas Krauch, Yapeng Gao, Andreas Zell. ICRA 2021.
• Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation. [pdf]
  • Yao Yao, Li Xiao, Zhicheng An, Wanpeng Zhang, Dijun Luo. ICRA 2021.
• Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction. [pdf]
  • Masashi Okada, Tadahiro Taniguchi. ICRA 2021.
• Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning. [pdf]
  • Kaiqi Chen, Yong Lee, Harold Soh. ICRA 2021.
• Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning. [pdf]
  • Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi. ICRA 2021.
• ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation. [pdf]
  • Fei Xia, Chengshu Li, Roberto Martín-Martín, Or Litany, Alexander Toshev, Silvio Savarese. ICRA 2021.
• Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture. [pdf]
  • Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei Hong, Chun-Yi Lee. ICRA 2021.
• Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty. [pdf]
  • Fanfei Chen, Paul Szenher, Yewei Huang, Jinkun Wang, Tixiao Shan, Shi Bai, Brendan J. Englot. ICRA 2021.
• Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour. [pdf]
  • Xihan Bian, Oscar Mendez Maldonado, Simon Hadfield. ICRA 2021.
• Real-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning. [pdf]
  • Siddhant Gangapurwala, Mathieu Geisert, Romeo Orsolino, Maurice F. Fallon, Ioannis Havoutis. ICRA 2021.
• DWA-RL: Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation among Mobile Obstacles. [pdf]
  • Utsav Patel, Nithish K. Sanjeev Kumar, Adarsh Jagan Sathyamoorthy, Dinesh Manocha. ICRA 2021.
• Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships. [pdf]
  • Xiaobai Ma, Jiachen Li, Mykel J. Kochenderfer, David Isele, Kikuo Fujimura. ICRA 2021.
• Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models. [pdf]
  • Yuchen Wu, Melissa Mozifian, Florian Shkurti. ICRA 2021.
• DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies. [pdf]
  • Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair, Alexander Khazatsky, Glen Berseth, Sergey Levine. ICRA 2021.
• LASER: Learning a Latent Action Space for Efficient Reinforcement Learning. [pdf]
  • Arthur Allshire, Roberto Martín-Martín, Charles Lin, Shawn Manuel, Silvio Savarese, Animesh Garg. ICRA 2021.
• Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention. [pdf]
  • Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, Sergey Levine. ICRA 2021.
• Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning. [pdf]
  • Andrew S. Morgan, Daljeet Nandha, Georgia Chalvatzaki, Carlo D'Eramo, Aaron M. Dollar, Jan Peters. ICRA 2021.
• Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning. [pdf]
  • Zih-Yun Chiu, Florian Richter, Emily K. Funk, Ryan K. Orosco, Michael C. Yip. ICRA 2021.
• NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments. [pdf]
  • Daniel Dugas, Juan I. Nieto, Roland Siegwart, Jen Jen Chung. ICRA 2021.
• Remote-Center-of-Motion Recommendation toward Brain Needle Intervention Using Deep Reinforcement Learning. [pdf]
  • Huxin Gao, Xiao Xiao, Liang Qiu, Max Q.-H. Meng, Nicolas Kon Kam King, Hongliang Ren. ICRA 2021.
• Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning. [pdf]
  • Keyu Li, Jian Wang, Yangxin Xu, Hao Qin, Dongsheng Liu, Li Liu, Max Q.-H. Meng. ICRA 2021.
• Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning. [pdf]
  • Tianhao Zhang, Yueheng Li, Shuai Li, Qiwei Ye, Chen Wang, Guangming Xie. ICRA 2021.
• An Adaptive Fuzzy Reinforcement Learning Cooperative Approach for the Autonomous Control of Flock Systems. [pdf]
  • Shuzheng Qu, Mohammed I. Abouheaf, Wail Gueaieb, Davide Spinello. ICRA 2021.
• Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning. [pdf]
  • Yunlong Song, HaoChih Lin, Elia Kaufmann, Peter Dürr, Davide Scaramuzza. ICRA 2021.
• Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee. [pdf]
  • Liang Hu, Yujie Tang, Zhipeng Zhou, Wei Pan. ICRA 2021.
• A Data-Driven Reinforcement Learning Solution Framework for Optimal and Adaptive Personalization of a Hip Exoskeleton. [pdf]
  • Xikai Tu, Minhan Li, Ming Liu, Jennie Si, He Helen Huang. ICRA 2021.
• FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimizer. [pdf]
  • Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How. ICRA 2021.
• Coding for Distributed Multi-Agent Reinforcement Learning. [pdf]
  • Baoqian Wang, Junfei Xie, Nikolay Atanasov. ICRA 2021.
• Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives. [pdf]
  • Alper Kamil Bozkurt, Yu Wang, Michael M. Zavlanos, Miroslav Pajic. ICRA 2021.
• Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning. [pdf]
  • Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic. ICRA 2021.
• Hierarchies of Planning and Reinforcement Learning for Robot Navigation. [pdf]
  • Jan Wöhlke, Felix Schmitt, Herke van Hoof. ICRA 2021.
• Context-Aware Safe Reinforcement Learning for Non-Stationary Environments. [pdf]
  • Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Liang Li, Ding Zhao. ICRA 2021.
• Quantification of Joint Redundancy considering Dynamic Feasibility using Deep Reinforcement Learning. [pdf]
  • Jiazheng Chai, Mitsuhiro Hayashibe. ICRA 2021.
• A Novel Hybrid Approach for Fault-Tolerant Control of UAVs based on Robust Reinforcement Learning. [pdf]
  • Yves Sohege, Marcos Quiñones-Grueiro, Gregory M. Provan. ICRA 2021.
• Using Reinforcement Learning to Create Control Barrier Functions for Explicit Risk Mitigation in Adversarial Environments. [pdf]
  • Edvards Scukins, Petter Ögren. ICRA 2021.
• Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning. [pdf]
  • Keuntaek Lee, Bogdan I. Vlahov, Jason Gibson, James M. Rehg, Evangelos A. Theodorou. ICRA 2021.
• Extendable Navigation Network based Reinforcement Learning for Indoor Robot Exploration. [pdf]
  • Woo-Cheol Lee, Ming Chong Lim, Han-Lim Choi. ICRA 2021.
• Deep Reinforcement Learning Framework for Underwater Locomotion of Soft Robot. [pdf]
  • Guanda Li, Jun Shintake, Mitsuhiro Hayashibe. ICRA 2021.
• Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks. [pdf]
  • Pin Wang, Hanhan Li, Ching-Yao Chan. ICRA 2021.
• Generalization in Reinforcement Learning by Soft Data Augmentation. [pdf]
  • Nicklas Hansen, Xiaolong Wang. ICRA 2021.
• Autonomous Multi-View Navigation via Deep Reinforcement Learning. [pdf]
  • Xueqin Huang, Wei Chen, Wei Zhang, Ran Song, Jiyu Cheng, Yibin Li. ICRA 2021.
• Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning. [pdf]
  • Patrick Wenzel, Torsten Schön, Laura Leal-Taixé, Daniel Cremers. ICRA 2021.

International Joint Conference on Artificial Intelligence

• Mean Field Games Flock! The Reinforcement Learning Way. [pdf]
  • Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin. IJCAI 2021.
• Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning. [pdf]
  • Jiawei Wang, Lijun Sun. IJCAI 2021.
• Data-Efficient Reinforcement Learning for Malaria Control. [pdf]
  • Lixin Zou. IJCAI 2021.
• Multi-Objective Reinforcement Learning for Designing Ethical Environments. [pdf]
  • Manel Rodriguez-Soto, Maite López-Sánchez, Juan A. Rodríguez-Aguilar. IJCAI 2021.
• Efficient PAC Reinforcement Learning in Regular Decision Processes. [pdf]
  • Alessandro Ronca, Giuseppe De Giacomo. IJCAI 2021.
• Deep Reinforcement Learning for Navigation in AAA Video Games. [pdf]
  • Eloi Alonso, Maxim Peter, David Goumard, Joshua Romoff. IJCAI 2021.
• Verifying Reinforcement Learning up to Infinity. [pdf]
  • Edoardo Bacci, Mirco Giacobbe, David Parker. IJCAI 2021.
• Robustly Learning Composable Options in Deep Reinforcement Learning. [pdf]
  • Akhil Bagaria, Jason K. Senthil, Matthew Slivinski, George Konidaris. IJCAI 2021.
• Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment. [pdf]
  • Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh. IJCAI 2021.
• Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots. [pdf]
  • Huiqiao Fu, Kaiqiang Tang, Peng Li, Wenqi Zhang, Xinpeng Wang, Guizhou Deng, Tao Wang, Chunlin Chen. IJCAI 2021.
• Model-Based Reinforcement Learning for Infinite-Horizon Discounted Constrained Markov Decision Processes. [pdf]
  • Aria HasanzadeZonuzy, Dileep M. Kalathil, Srinivas Shakkottai. IJCAI 2021.
• Reinforcement Learning for Route Optimization with Robustness Guarantees. [pdf]
  • Tobias Jacobs, Francesco Alesiani, Gülcin Ermis. IJCAI 2021.
• Average-Reward Reinforcement Learning with Trust Region Methods. [pdf]
  • Xiaoteng Ma, Xiaohang Tang, Li Xia, Jun Yang, Qianchuan Zhao. IJCAI 2021.
• Meta-Reinforcement Learning by Tracking Task Non-stationarity. [pdf]
  • Riccardo Poiani, Andrea Tirinzoni, Marcello Restelli. IJCAI 2021.
• Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market. [pdf]
  • Dawei Qiu, Jianhong Wang, Junkai Wang, Goran Strbac. IJCAI 2021.
• Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models. [pdf]
  • Zeyuan Wang, Chaofeng Sha, Su Yang. IJCAI 2021.
• Deep Reinforcement Learning Boosted Partial Domain Adaptation. [pdf]
  • Keyu Wu, Min Wu, Jianfei Yang, Zhenghua Chen, Zhengguo Li, Xiaoli Li. IJCAI 2021.
• Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning. [pdf]
  • Fan Zhou, Zhoufan Zhu, Qi Kuang, Liwen Zhang. IJCAI 2021.
• Ordering-Based Causal Discovery with Reinforcement Learning. [pdf]
  • Xiaoqiang Wang, Yali Du, Shengyu Zhu, Liangjun Ke, Zhitang Chen, Jianye Hao, Jun Wang. IJCAI 2021.
• Boosting Offline Reinforcement Learning with Residual Generative Modeling. [pdf]
  • Hua Wei, Deheng Ye, Zhao Liu, Hao Wu, Bo Yuan, Qiang Fu, Wei Yang, Zhenhui Li. IJCAI 2021.
• Dynamic Lane Traffic Signal Control with Group Attention and Multi-Timescale Reinforcement Learning. [pdf]
  • Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng. IJCAI 2021.
• BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning. [pdf]
  • Lun Wang, Zaynah Javed, Xian Wu, Wenbo Guo, Xinyu Xing, Dawn Song. IJCAI 2021.
• Objective-aware Traffic Simulation via Inverse Reinforcement Learning. [pdf]
  • Guanjie Zheng, Hanyang Liu, Kai Xu, Zhenhui Li. IJCAI 2021.
• Policy Learning with Constraints in Model-free Reinforcement Learning: A Survey. [pdf]
  • Yongshuai Liu, Avishai Halev, Xin Liu. IJCAI 2021.
• Deep Residual Reinforcement Learning (Extended Abstract). [pdf]
  • Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson. IJCAI 2021.
• Planning and Reinforcement Learning for General-Purpose Service Robots. [pdf]
  • Yuqian Jiang. IJCAI 2021.
• Deep Reinforcement Learning with Hierarchical Structures. [pdf]
  • Siyuan Li. IJCAI 2021.
• Combining Reinforcement Learning and Causal Models for Robotics Applications. [pdf]
  • Arquímides Méndez-Molina. IJCAI 2021.
• Inter-Task Similarity for Lifelong Reinforcement Learning in Heterogeneous Tasks. [pdf]
  • Sergio A. Serrano. IJCAI 2021.
• Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning. [pdf]
  • Nir Lipovetzky. IJCAI 2021.

Annual Conference on Neural Information Processing Systems

• Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. [pdf]
  • Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert. NeurIPS 2021.
• Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. [pdf]
  • Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling. NeurIPS 2021.
• Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. [pdf]
  • Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low. NeurIPS 2021.
• Risk-Averse Bayes-Adaptive Reinforcement Learning. [pdf]
  • Marc Rigter, Bruno Lacerda, Nick Hawes. NeurIPS 2021.
• Offline Reinforcement Learning as One Big Sequence Modeling Problem. [pdf]
  • Michael Janner, Qiyang Li, Sergey Levine. NeurIPS 2021.
• Distributional Reinforcement Learning for Multi-Dimensional Reward Functions. [pdf]
  • Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu. NeurIPS 2021.
• A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. [pdf]
  • Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio. NeurIPS 2021.
• Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. [pdf]
  • Weitong Zhang, Dongruo Zhou, Quanquan Gu. NeurIPS 2021.
• There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. [pdf]
  • Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist. NeurIPS 2021.
• Reinforcement Learning in Reward-Mixing MDPs. [pdf]
  • Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor. NeurIPS 2021.
• Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning. [pdf]
  • Kibeom Kim, Min Whoo Lee, Yoonsung Kim, Je-Hwan Ryu, Min Su Lee, Byoung-Tak Zhang. NeurIPS 2021.
• On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. [pdf]
  • Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman E. Ozdaglar. NeurIPS 2021.
• Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks. [pdf]
  • Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C. Green. NeurIPS 2021.
• On the Theory of Reinforcement Learning with Once-per-Episode Feedback. [pdf]
  • Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan. NeurIPS 2021.
• On Effective Scheduling of Model-based Reinforcement Learning. [pdf]
  • Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li. NeurIPS 2021.
• Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. [pdf]
  • Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong. NeurIPS 2021.
• Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration. [pdf]
  • Lulu Zheng, Jiarui Chen, Jianhao Wang, Jiamin He, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao, Chongjie Zhang. NeurIPS 2021.
• Information Directed Reward Learning for Reinforcement Learning. [pdf]
  • David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause. NeurIPS 2021.
• Celebrating Diversity in Shared Multi-Agent Reinforcement Learning. [pdf]
  • Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, Chongjie Zhang. NeurIPS 2021.
• Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. [pdf]
  • Ming Yin, Yu-Xiang Wang. NeurIPS 2021.
• Environment Generation for Zero-Shot Compositional Reinforcement Learning. [pdf]
  • Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust. NeurIPS 2021.
• Offline Meta Reinforcement Learning - Identifiability Challenges and Effective Data Collection Strategies. [pdf]
  • Ron Dorfman, Idan Shenfeld, Aviv Tamar. NeurIPS 2021.
• PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning. [pdf]
  • Tao Yu, Cuiling Lan, Wenjun Zeng, Mingxiao Feng, Zhizheng Zhang, Zhibo Chen. NeurIPS 2021.
• Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. [pdf]
  • Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko. NeurIPS 2021.
• Automatic Data Augmentation for Generalization in Reinforcement Learning. [pdf]
  • Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus. NeurIPS 2021.
• RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem. [pdf]
  • Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E. Gonzalez, Ion Stoica. NeurIPS 2021.
• Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning. [pdf]
  • Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho. NeurIPS 2021.
• Bellman-consistent Pessimism for Offline Reinforcement Learning. [pdf]
  • Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal. NeurIPS 2021.
• Teachable Reinforcement Learning via Advice Distillation. [pdf]
  • Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas. NeurIPS 2021.
• Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees. [pdf]
  • Gregory Dexter, Kevin Bello, Jean Honorio. NeurIPS 2021.
• Online Robust Reinforcement Learning with Model Uncertainty. [pdf]
  • Yue Wang, Shaofeng Zou. NeurIPS 2021.
• Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble. [pdf]
  • Gaon An, Seungyong Moon, Jang-Hyun Kim, Hyun Oh Song. NeurIPS 2021.
• A Provably Efficient Sample Collection Strategy for Reinforcement Learning. [pdf]
  • Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric. NeurIPS 2021.
• Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. [pdf]
  • Ming Yin, Yu Bai, Yu-Xiang Wang. NeurIPS 2021.
• Multi-Agent Reinforcement Learning in Stochastic Networked Systems. [pdf]
  • Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman. NeurIPS 2021.
• When Is Generalizable Reinforcement Learning Tractable? [pdf]
  • Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar. NeurIPS 2021.
• Learning Markov State Abstractions for Deep Reinforcement Learning. [pdf]
  • Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris. NeurIPS 2021.
• Towards Deeper Deep Reinforcement Learning with Spectral Normalization. [pdf]
  • Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger. NeurIPS 2021.
• Adversarial Intrinsic Motivation for Reinforcement Learning. [pdf]
  • Ishan Durugkar, Mauricio Tec, Scott Niekum, Peter Stone. NeurIPS 2021.
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning. [pdf]
  • Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe. NeurIPS 2021.
• TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning. [pdf]
  • Minchao Wu, Michael Norrish, Christian Walder, Amir Dezfouli. NeurIPS 2021.
• Model-Based Reinforcement Learning via Imagination with Derived Memory. [pdf]
  • Yao Mu, Yuzheng Zhuang, Bin Wang, Guangxiang Zhu, Wulong Liu, Jianyu Chen, Ping Luo, Shengbo Li, Chongjie Zhang, Jianye Hao. NeurIPS 2021.
• Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning. [pdf]
  • Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Shane Gu. NeurIPS 2021.
• Compositional Reinforcement Learning from Logical Specifications. [pdf]
  • Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur. NeurIPS 2021.
• Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning. [pdf]
  • Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, Qianchuan Zhao. NeurIPS 2021.
• Local Differential Privacy for Regret Minimization in Reinforcement Learning. [pdf]
  • Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta. NeurIPS 2021.
• Continuous Doubly Constrained Batch Reinforcement Learning. [pdf]
  • Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J. Smola. NeurIPS 2021.
• Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. [pdf]
  • Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn. NeurIPS 2021.
• Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. [pdf]
  • Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell. NeurIPS 2021.
• A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. [pdf]
  • Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert. NeurIPS 2021.
• Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning. [pdf]
  • Ali Taghibakhshi, Scott P. MacLachlan, Luke N. Olson, Matthew West. NeurIPS 2021.
• EDGE: Explaining Deep Reinforcement Learning Policies. [pdf]
  • Wenbo Guo, Xian Wu, Usmann Khan, Xinyu Xing. NeurIPS 2021.
• Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning. [pdf]
  • Guanlin Liu, Lifeng Lai. NeurIPS 2021.
• Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning. [pdf]
  • Xiong-Hui Chen, Shengyi Jiang, Feng Xu, Zongzhang Zhang, Yang Yu. NeurIPS 2021.
• Pretraining Representations for Data-Efficient Reinforcement Learning. [pdf]
  • Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville. NeurIPS 2021.
• Tactical Optimism and Pessimism for Deep Reinforcement Learning. [pdf]
  • Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan. NeurIPS 2021.
• Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning. [pdf]
  • Siyuan Zhang, Nan Jiang. NeurIPS 2021.
• Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. [pdf]
  • Ming Yin, Yu-Xiang Wang. NeurIPS 2021.
• Outcome-Driven Reinforcement Learning via Variational Inference. [pdf]
  • Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine. NeurIPS 2021.
• Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. [pdf]
  • Jingfeng Wu, Vladimir Braverman, Lin Yang. NeurIPS 2021.
• Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. [pdf]
  • Tianhao Wang, Dongruo Zhou, Quanquan Gu. NeurIPS 2021.
• Heuristic-Guided Reinforcement Learning. [pdf]
  • Ching-An Cheng, Andrey Kolobov, Adith Swaminathan. NeurIPS 2021.
• Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. [pdf]
  • Andrea Zanette, Martin J. Wainwright, Emma Brunskill. NeurIPS 2021.
• Safe Reinforcement Learning with Natural Language Constraints. [pdf]
  • Tsung-Yen Yang, Michael Y. Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan. NeurIPS 2021.
• Safe Reinforcement Learning by Imagining the Near Future. [pdf]
  • Garrett Thomas, Yuping Luo, Tengyu Ma. NeurIPS 2021.
• Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. [pdf]
  • Jiafan He, Dongruo Zhou, Quanquan Gu. NeurIPS 2021.
• MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents. [pdf]
  • Stephen Chung. NeurIPS 2021.
• PettingZoo: Gym for Multi-Agent Reinforcement Learning. [pdf]
  • Justin K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis S. Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, Niall L. Williams, Yashas Lokesh, Praveen Ravi. NeurIPS 2021.
• Decision Transformer: Reinforcement Learning via Sequence Modeling. [pdf]
  • Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch. NeurIPS 2021.
• Nearly Horizon-Free Offline Reinforcement Learning. [pdf]
  • Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi. NeurIPS 2021.
• Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes. [pdf]
  • Hyunji Alex Nam, Scott L. Fleming, Emma Brunskill. NeurIPS 2021.
• Contrastive Reinforcement Learning of Symbolic Reasoning Domains. [pdf]
  • Gabriel Poesia, Wenxin Dong, Noah D. Goodman. NeurIPS 2021.
• Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. [pdf]
  • Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta. NeurIPS 2021.
• Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting. [pdf]
  • Gen Li, Yuxin Chen, Yuejie Chi, Yuantao Gu, Yuting Wei. NeurIPS 2021.
• Scalable Online Planning via Reinforcement Learning Fine-Tuning. [pdf]
  • Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart J. Russell, Noam Brown. NeurIPS 2021.
• An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning. [pdf]
  • Tianpei Yang, Weixun Wang, Hongyao Tang, Jianye Hao, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yingfeng Chen, Yujing Hu, Changjie Fan, Chengwei Zhang. NeurIPS 2021.
• Risk-Aware Transfer in Reinforcement Learning using Successor Features. [pdf]
  • Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee. NeurIPS 2021.
• Regret Minimization Experience Replay in Off-Policy Reinforcement Learning. [pdf]
  • Xu-Hui Liu, Zhenghai Xue, Jing-Cheng Pang, Shengyi Jiang, Feng Xu, Yang Yu. NeurIPS 2021.
• Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning. [pdf]
  • Gen Li, Laixi Shi, Yuxin Chen, Yuantao Gu, Yuejie Chi. NeurIPS 2021.
• A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning. [pdf]
  • Gugan Thoppe, Bhumesh Kumar. NeurIPS 2021.
• Autonomous Reinforcement Learning via Subgoal Curricula. [pdf]
  • Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn. NeurIPS 2021.
• PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators. [pdf]
  • Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang. NeurIPS 2021.
• Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu. NeurIPS 2021.
• Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. [pdf]
  • Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan. NeurIPS 2021.
• Functional Regularization for Reinforcement Learning via Learned Fourier Features. [pdf]
  • Alexander C. Li, Deepak Pathak. NeurIPS 2021.
• Agent Modelling under Partial Observability for Deep Reinforcement Learning. [pdf]
  • Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht. NeurIPS 2021.
• Conservative Offline Distributional Reinforcement Learning. [pdf]
  • Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani. NeurIPS 2021.
• Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning. [pdf]
  • Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart. NeurIPS 2021.
• Explicable Reward Design for Reinforcement Learning Agents. [pdf]
  • Rati Devidze, Goran Radanovic, Parameswaran Kamalaruban, Adish Singla. NeurIPS 2021.
• A Minimalist Approach to Offline Reinforcement Learning. [pdf]
  • Scott Fujimoto, Shixiang Shane Gu. NeurIPS 2021.
• BCORLE(λ): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market. [pdf]
  • Yang Zhang, Bo Tang, Qingyu Yang, Dou An, Hongyin Tang, Chenyang Xi, Xueying Li, Feiyu Xiong. NeurIPS 2021.
• Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning. [pdf]
  • Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang. NeurIPS 2021.
• Reinforcement Learning based Disease Progression Model for Alzheimer's Disease. [pdf]
  • Krishnakant V. Saboo, Anirudh Choudhary, Yurui Cao, Gregory A. Worrell, David T. Jones, Ravishankar K. Iyer. NeurIPS 2021.
• Accelerating Quadratic Optimization with Reinforcement Learning. [pdf]
  • Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E. Gonzalez, Ion Stoica, Ken Goldberg. NeurIPS 2021.
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data. [pdf]
  • Lingxiao Wang, Zhuoran Yang, Zhaoran Wang. NeurIPS 2021.
• Hierarchical Reinforcement Learning with Timed Subgoals. [pdf]
  • Nico Gürtler, Dieter Büchler, Georg Martius. NeurIPS 2021.
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives. [pdf]
  • Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov. NeurIPS 2021.
• Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation. [pdf]
  • Lin Guan, Mudit Verma, Sihang Guo, Ruohan Zhang, Subbarao Kambhampati. NeurIPS 2021.
• Reinforcement Learning in Newcomblike Environments. [pdf]
  • James Bell, Linda Linsefors, Caspar Oesterheld, Joar Skalse. NeurIPS 2021.
• Reinforcement Learning with Latent Flow. [pdf]
  • Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin. NeurIPS 2021.
• Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. [pdf]
  • Jiafan He, Dongruo Zhou, Quanquan Gu. NeurIPS 2021.
• Reinforcement Learning Enhanced Explainer for Graph Neural Networks. [pdf]
  • Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, Dongsheng Li. NeurIPS 2021.
• The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. [pdf]
  • Yujin Tang, David Ha. NeurIPS 2021.
• Causal Influence Detection for Improving Efficiency in Reinforcement Learning. [pdf]
  • Maximilian Seitzer, Bernhard Schölkopf, Georg Martius. NeurIPS 2021.
• Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model. [pdf]
  • Bingyan Wang, Yuling Yan, Jianqing Fan. NeurIPS 2021.
• RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents. [pdf]
  • Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich. NeurIPS 2021.
• The Difficulty of Passive Learning in Deep Reinforcement Learning. [pdf]
  • Georg Ostrovski, Pablo Samuel Castro, Will Dabney. NeurIPS 2021.
• A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems. [pdf]
  • Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Xialiang Tong, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng. NeurIPS 2021.
• Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. [pdf]
  • T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen. NeurIPS 2021.
• Machine versus Human Attention in Deep Reinforcement Learning Tasks. [pdf]
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