Welcome to our GitHub repository! This repository is dedicated to curating significant research papers in the field of Reinforcement Learning (RL) that have been accepted at top academic conferences such as AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more. We provide you with a convenient resource hub to help you stay updated on the latest developments in reinforcement learning, delve into research trends, and explore cutting-edge algorithms and methods.

• 2023/11/12: I added the related repository.
• 2023/8/19: I added papers accepted at AAMAS'23, IJCAI'23, ICRA'23, ICML'23,ICLR'23, AAAI'23, NeurIPS'22 etc
• 2023/1/6: I created the repository.

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 📮.

• 1_Multi-Agent Reinforcement Learning (MARL)
• 2_Meta Reinforcement Learning (Meta RL)
• 3_Hierarchical Reinforcement Learning (HRL)
• 4_Multi-Task Reinforcement Learning (Multi-Task RL)
• 5_Offline Reinforcement Learning (Offline RL)
• 6_Inverse Reinforcement Learning (IRL)
• 7_Reinforcement Learning with Large Language Models

• Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning. [pdf]

  • Jiechuan Jiang, Zongqing Lu. AAAI 2023.

• Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. [pdf]

  • Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie. AAAI 2023.

• Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning. [pdf]

  • Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo. AAAI 2023.

• DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Zhaoxing Yang, Haiming Jin, Rong Ding, Haoyi You, Guiyun Fan, Xinbing Wang, Chenghu Zhou. AAAI 2023.

• Quantum Multi-Agent Meta Reinforcement Learning. [pdf]

  • Won Joon Yun, Jihong Park, Joongheon Kim. AAAI 2023.

• Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient. [pdf]

  • Wubing Chen, Wenbin Li, Xiao Liu, Shangdong Yang, Yang Gao. AAAI 2023.

• Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning. [pdf]

  • Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang. AAAI 2023.

• DM²: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching. [pdf]

  • Caroline Wang, Ishan Durugkar, Elad Liebman, Peter Stone. AAAI 2023.

• Consensus Learning for Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Zhiwei Xu, Bin Zhang, Dapeng Li, Zeren Zhang, Guangchong Zhou, Hao Chen, Guoliang Fan. AAAI 2023.

• HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism. [pdf]

  • Zhiwei Xu, Yunpeng Bai, Bin Zhang, Dapeng Li, Guoliang Fan. AAAI 2023.

• DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning. [pdf]

  • Tingting Yuan, Hwei-Ming Chung, Jie Yuan, Xiaoming Fu. AAAI 2023.

• Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Ronghui Mu, Wenjie Ruan, Leandro Soriano Marcolino, Gaojie Jin, Qiang Ni. AAAI 2023.

• Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning With Applications to Ridesharing. [pdf]

  • Lucia Cipolina-Kun. AAAI 2023.

• Tackling Safe and Efficient Multi-Agent Reinforcement Learning via Dynamic Shielding (Student Abstract). [pdf]

  • Wenli Xiao, Yiwei Lyu, John M. Dolan. AAAI 2023.

• Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. [pdf]

  • Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson. AAMAS 2023.

• Adaptive Learning Rates for Multi-Agent Reinforcement Learning. [pdf]

  • Jiechuan Jiang, Zongqing Lu. AAMAS 2023.

• Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Shanqi Liu, Yujing Hu, Runze Wu, Dong Xing, Yu Xiong, Changjie Fan, Kun Kuang, Yong Liu. AAMAS 2023.

• A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning. [pdf]

  • Woojun Kim, Whiyoung Jung, Myungsik Cho, Youngchul Sung. AAMAS 2023.

• Mediated Multi-Agent Reinforcement Learning. [pdf]

  • Dmitry Ivanov, Ilya Zisman, Kirill Chernyshev. AAMAS 2023.

• EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning. [pdf]

  • Yucong Zhang, Chao Yu. AAMAS 2023.

• AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning. [pdf]

  • Xuefeng Wang, Xinran Li, Jiawei Shao, Jun Zhang. AAMAS 2023.

• Learning Structured Communication for Multi-Agent Reinforcement Learning. [pdf]

  • Junjie Sheng, Xiangfeng Wang, Bo Jin, Wenhao Li, Jun Wang, Junchi Yan, Tsung-Hui Chang, Hongyuan Zha. AAMAS 2023.

• Model-based Sparse Communication in Multi-agent Reinforcement Learning. [pdf]

  • Shuai Han, Mehdi Dastani, Shihan Wang. AAMAS 2023.

• Sequential Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Yifan Zang, Jinmin He, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing. AAMAS 2023.

• Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration. [pdf]

  • Chao Yu, Xinyi Yang, Jiaxuan Gao, Jiayu Chen, Yunfei Li, Jijia Liu, Yunfei Xiang, Ruixin Huang, Huazhong Yang, Yi Wu, Yu Wang. AAMAS 2023.

• Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. [pdf]

  • Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley. AAMAS 2023.

• CraftEnv: A Flexible Collective Robotic Construction Environment for Multi-Agent Reinforcement Learning. [pdf]

  • Rui Zhao, Xu Liu, Yizheng Zhang, Minghao Li, Cheng Zhou, Shuai Li, Lei Han. AAMAS 2023.

• Multi-Agent Reinforcement Learning with Safety Layer for Active Voltage Control. [pdf]

  • Yufeng Shi, Mingxiao Feng, Minrui Wang, Wengang Zhou, Houqiang Li. AAMAS 2023.

• Model-based Dynamic Shielding for Safe and Efficient Multi-agent Reinforcement Learning. [pdf]

  • Wenli Xiao, Yiwei Lyu, John M. Dolan. AAMAS 2023.

• Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Jihwan Oh, Joonkee Kim, Minchan Jeong, Se-Young Yun. AAMAS 2023.

• Counterexample-Guided Policy Refinement in Multi-Agent Reinforcement Learning. [pdf]

  • Briti Gangopadhyay, Pallab Dasgupta, Soumyajit Dey. AAMAS 2023.

• Prioritized Tasks Mining for Multi-Task Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Yang Yu, Qiyue Yin, Junge Zhang, Kaiqi Huang. AAMAS 2023.

• TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems. [pdf]

  • Matteo Gallici, Mario Martin, Ivan Masmitja. AAMAS 2023.

• Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning. [pdf]

  • Woojun Kim, Youngchul Sung. AAMAS 2023.

• Towards Explaining Sequences of Actions in Multi-Agent Deep Reinforcement Learning Models. [pdf]

  • Khaing Phyo Wai, Minghong Geng, Budhitama Subagdja, Shubham Pateria, Ah-Hwee Tan. AAMAS 2023.

• Multi-Agent Deep Reinforcement Learning for High-Frequency Multi-Market Making. [pdf]

  • Pankaj Kumar. AAMAS 2023.

• Learning Individual Difference Rewards in Multi-Agent Reinforcement Learning. [pdf]

  • Chen Yang, Guangkai Yang, Junge Zhang. AAMAS 2023.

• Off-Beat Multi-Agent Reinforcement Learning. [pdf]

  • Wei Qiu, Weixun Wang, Rundong Wang, Bo An, Yujing Hu, Svetlana Obraztsova, Zinovi Rabinovich, Jianye Hao, Yingfeng Chen, Changjie Fan. AAMAS 2023.

• Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning. [pdf]

  • Matthias Gerstgrasser, Tom Danino, Sarah Keren. AAMAS 2023.

• Off-the-Grid MARL: Datasets and Baselines for Offline Multi-Agent Reinforcement Learning. [pdf]

  • Claude Formanek, Asad Jeewa, Jonathan P. Shock, Arnu Pretorius. AAMAS 2023.

• Grey-box Adversarial Attack on Communication in Multi-agent Reinforcement Learning. [pdf]

  • Xiao Ma, Wu-Jun Li. AAMAS 2023.

• Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads. [pdf]

  • Vincent Mai, Philippe Maisonneuve, Tianyu Zhang, Hadi Nekoei, Liam Paull, Antoine Lesage-Landry. AAMAS 2023.

• Learning to Self-Reconfigure for Freeform Modular Robots via Altruism Multi-Agent Reinforcement Learning. [pdf]

  • Lei Wu, Bin Guo, Qiuyun Zhang, Zhuo Sun, Jieyi Zhang, Zhiwen Yu. AAMAS 2023.

• Multi-Agent Path Finding via Reinforcement Learning with Hybrid Reward. [pdf]

  • Cheng Zhao, Liansheng Zhuang, Haonan Liu, Yihong Huang, Jian Yang. AAMAS 2023.

• Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning. [pdf]

  • Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng. AAMAS 2023.

• Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization. [pdf]

  • Xiangsen Wang, Xianyuan Zhan. AAMAS 2023.

• Causality Detection for Efficient Multi-Agent Reinforcement Learning. [pdf]

  • Rafael Pina, Varuna De Silva, Corentin Artaud. AAMAS 2023.

• Attention-Based Recurrency for Multi-Agent Reinforcement Learning under State Uncertainty. [pdf]

  • Thomy Phan, Fabian Ritz, Jonas Nüßlein, Michael Kölle, Thomas Gabor, Claudia Linnhoff-Popien. AAMAS 2023.

• Fair Transport Network Design using Multi-Agent Reinforcement Learning. [pdf]

  • Dimitris Michailidis. AAMAS 2023.

• Reinforcement Learning in Multi-Objective Multi-Agent Systems. [pdf]

  • Willem Röpke. AAMAS 2023.

• Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning. [pdf]

  • Lucia Cipolina-Kun. AAMAS 2023.

• Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio. ICLR 2023.

• MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection. [pdf]

  • Jiaxun Cui, Xiaomeng Yang, Mulong Luo, Geunbae Lee, Peter Stone, Hsien-Hsin S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong, Yuandong Tian. ICLR 2023.

• MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. [pdf]

  • Mikayel Samvelyan, Akbir Khan, Michael Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster, Roberta Raileanu, Tim Rocktäschel. ICLR 2023.

• Scaling Laws for a Multi-Agent Reinforcement Learning Model. [pdf]

  • Oren Neumann, Claudius Gros. ICLR 2023.

• RPM: Generalizable Multi-Agent Policies for Multi-Agent Reinforcement Learning. [pdf]

  • Wei Qiu, Xiao Ma, Bo An, Svetlana Obraztsova, Shuicheng Yan, Zhongwen Xu. ICLR 2023.

• Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning. [pdf]

  • Yat Long Lo, Christian Schröder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, Shimon Whiteson. ICLR 2023.

• Order Matters: Agent-by-agent Policy Optimization. [pdf]

  • Xihuai Wang, Zheng Tian, Ziyu Wan, Ying Wen, Jun Wang, Weinan Zhang. ICLR 2023.

• Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Dingyang Chen, Qi Zhang. ICML 2023.

• Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning. [pdf]

  • Ziluo Ding, Wanpeng Zhang, Junpeng Yue, Xiangjun Wang, Tiejun Huang, Zongqing Lu. ICML 2023.

• Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning. [pdf]

  • Matthias Gerstgrasser, David C. Parkes. ICML 2023.

• An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning. [pdf]

  • Woojun Kim, Youngchul Sung. ICML 2023.

• RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution. [pdf]

  • Pengyi Li, Jianye Hao, Hongyao Tang, Yan Zheng, Xian Fu. ICML 2023.

• Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning. [pdf]

  • Boyin Liu, Zhiqiang Pu, Yi Pan, Jianqiang Yi, Yanyan Liang, Du Zhang. ICML 2023.

• Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. [pdf]

  • Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu. ICML 2023.

• Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation. [pdf]

  • Siddharth Nayak, Kenneth Choi, Wenqi Ding, Sydney Dolan, Karthik Gopalakrishnan, Hamsa Balakrishnan. ICML 2023.

• Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability. [pdf]

  • Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein, Michael Kölle, Thomas Gabor, Claudia Linnhoff-Popien. ICML 2023.

• Complementary Attention for Multi-Agent Reinforcement Learning. [pdf]

  • Jianzhun Shao, Hongchang Zhang, Yun Qu, Chang Liu, Shuncheng He, Yuhang Jiang, Xiangyang Ji. ICML 2023.

• Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. [pdf]

  • Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee. ICML 2023.

• Multi-Target Pursuit by a Decentralized Heterogeneous UAV Swarm using Deep Multi-Agent Reinforcement Learning. [pdf]

  • Maryam Kouzeghar, Youngbin Song, Malika Meghjani, Roland Bouffanais. ICRA 2023.

• Explainable Action Advising for Multi-Agent Reinforcement Learning. [pdf]

  • Yue Guo, Joseph Campbell, Simon Stepputtis, Ruiyu Li, Dana Hughes, Fei Fang, Katia P. Sycara. ICRA 2023.

• Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios. [pdf]

  • Zhili Zhang, Songyang Han, Jiangwei Wang, Fei Miao. ICRA 2023.

• Conflict-constrained Multi-agent Reinforcement Learning Method for Parking Trajectory Planning. [pdf]

  • Siyuan Chen, Meiling Wang, Yi Yang, Wenjie Song. ICRA 2023.

• Explainable Multi-Agent Reinforcement Learning for Temporal Queries. [pdf]

  • Kayla Boggess, Sarit Kraus, Lu Feng. IJCAI 2023.

• Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism. [pdf]

  • Xudong Guo, Daming Shi, Wenhui Fan. IJCAI 2023.

• Learning to Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching and Rescheduling via Reinforcement Learning. [pdf]

  • Waldy Joe, Hoong Chuin Lau. IJCAI 2023.

• Decentralized Anomaly Detection in Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Kiarash Kazari, Ezzeldin Shereen, György Dán. IJCAI 2023.

• GPLight: Grouped Multi-agent Reinforcement Learning for Large-scale Traffic Signal Control. [pdf]

  • Yilin Liu, Guiyang Luo, Quan Yuan, Jinglin Li, Lei Jin, Bo Chen, Rui Pan. IJCAI 2023.

• Deep Hierarchical Communication Graph in Multi-Agent Reinforcement Learning. [pdf]

  • Zeyang Liu, Lipeng Wan, Xue Sui, Zhuoran Chen, Kewu Sun, Xuguang Lan. IJCAI 2023.

• Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning. [pdf]

  • Elizaveta Tennant, Stephen Hailes, Mirco Musolesi. IJCAI 2023.

• Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning. [pdf]

  • Bin Zhang, Lijuan Li, Zhiwei Xu, Dapeng Li, Guoliang Fan. IJCAI 2023.

• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning. [pdf]

  • Yinda Chen, Wei Huang, Shenglong Zhou, Qi Chen, Zhiwei Xiong. IJCAI 2023.

• MA2CL: Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning. [pdf]

  • Haolin Song, Mingxiao Feng, Wengang Zhou, Houqiang Li. IJCAI 2023.

• Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning. [pdf]

  • Xiaoli Tang, Han Yu. IJCAI 2023.

• DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning. [pdf]

  • Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li. IJCAI 2023.

• Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning. [pdf]
  • Mingyang Wang, Zhenshan Bing, Xiangtong Yao, Shuai Wang, Kai Huang, Hang Su, Chenguang Yang, Alois Knoll. AAAI 2023.
• Quantum Multi-Agent Meta Reinforcement Learning. [pdf]
  • Won Joon Yun, Jihong Park, Joongheon Kim. AAAI 2023.
• A CMDP-within-online framework for Meta-Safe Reinforcement Learning. [pdf]
  • Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei, Ming Jin. ICLR 2023.
• Distributional Meta-Gradient Reinforcement Learning. [pdf]
  • Haiyan Yin, Shuicheng Yan, Zhongwen Xu. ICLR 2023.
• Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning. [pdf]
  • Evan Zheran Liu, Sahaana Suri, Tong Mu, Allan Zhou, Chelsea Finn. ICML 2023.
• Offline Meta Reinforcement Learning with In-Distribution Online Adaptation. [pdf]
  • Jianhao Wang, Jin Zhang, Haozhe Jiang, Junyu Zhang, Liwei Wang, Chongjie Zhang. ICML 2023.
• Meta-Reinforcement Learning via Language Instructions. [pdf]
  • Zhenshan Bing, Alexander W. Koch, Xiangtong Yao, Kai Huang, Alois Knoll. ICRA 2023.
• Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning. [pdf]
  • Zheng Wu, Yichen Xie, Wenzhao Lian, Changhao Wang, Yanjiang Guo, Jianyu Chen, Stefan Schaal, Masayoshi Tomizuka. ICRA 2023.

• HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism. [pdf]
  • Zhiwei Xu, Yunpeng Bai, Bin Zhang, Dapeng Li, Guoliang Fan. AAAI 2023.
• Hierarchical Mean-Field Deep Reinforcement Learning for Large-Scale Multiagent Systems. [pdf]
  • Chao Yu. AAAI 2023.
• Hierarchical Reinforcement Learning with Human-AI Collaborative Sub-Goals Optimization. [pdf]
  • Haozhe Ma, Thanh Vinh Vo, Tze-Yun Leong. AAMAS 2023.
• Hierarchical Reinforcement Learning for Ad Hoc Teaming. [pdf]
  • Stéphane Aroca-Ouellette, Miguel Aroca-Ouellette, Upasana Biswas, Katharina Kann, Alessandro Roncone. AAMAS 2023.
• Matching Options to Tasks using Option-Indexed Hierarchical Reinforcement Learning. [pdf]
  • Kushal Chauhan, Soumya Chatterjee, Akash Reddy, Aniruddha S, Balaraman Ravindran, Pradeep Shenoy. AAMAS 2023.
• Hierarchical Reinforcement Learning with Attention Reward. [pdf]
  • Sihong Luo, Jinghao Chen, Zheng Hu, Chunhong Zhang, Benhui Zhuang. AAMAS 2023.
• Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs. [pdf]
  • Guan-Ting Liu, En-Pei Hu, Pu-Jen Cheng, Hung-Yi Lee, Shao-Hua Sun. ICML 2023.
• Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning. [pdf]
  • Kyowoon Lee, Seongun Kim, Jaesik Choi. ICRA 2023.

• PiCor: Multi-Task Deep Reinforcement Learning with Policy Correction. [pdf]
  • Fengshuo Bai, Hongming Zhang, Tianyang Tao, Zhiheng Wu, Yanna Wang, Bo Xu. AAAI 2023.
• Prioritized Tasks Mining for Multi-Task Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Yang Yu, Qiyue Yin, Junge Zhang, Kaiqi Huang. AAMAS 2023.
• Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. [pdf]
  • Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare. ICLR 2023.
• Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning. [pdf]
  • Abhishek Gupta, Corey Lynch, Brandon Kinman, Garrett Peake, Sergey Levine, Karol Hausman. ICRA 2023.

• Offline Quantum Reinforcement Learning in a Conservative Manner. [pdf]
  • Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao. AAAI Conference on Artificial Intelligence (AAAI 2023).
• On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples. [pdf]
  • Mustafa O. Karabag, Ufuk Topcu. AAAI Conference on Artificial Intelligence (AAAI 2023).

• Misspecification in Inverse Reinforcement Learning. [pdf]
  • Joar Skalse, Alessandro Abate. AAAI 2023.
• Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning. [pdf]
  • Haochen Wu, Pedro Sequeira, David V. Pynadath. AAMAS 2023.
• Adversarial Inverse Reinforcement Learning for Mean Field Games. [pdf]
  • Yang Chen, Libo Zhang, Jiamou Liu, Michael Witbrock. AAMAS 2023.
• LTL-Based Non-Markovian Inverse Reinforcement Learning. [pdf]
  • Mohammad Afzal, Sankalp Gambhir, Ashutosh Gupta, S. Krishna, Ashutosh Trivedi, Alvaro Velasquez. AAMAS 2023.
• LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. [pdf]
  • Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters. ICLR 2023.
• Causal Imitation Learning via Inverse Reinforcement Learning. [pdf]
  • Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim. ICLR 2023.
• Benchmarking Constraint Inference in Inverse Reinforcement Learning. [pdf]
  • Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart. ICLR 2023.
• CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning. [pdf]
  • Sheng Yue, Guanbo Wang, Wei Shao, Zhaofeng Zhang, Sen Lin, Ju Ren, Junshan Zhang. ICLR 2023.
• Multi-task Hierarchical Adversarial Inverse Reinforcement Learning. [pdf]
  • Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal. ICML 2023.
• Towards Theoretical Understanding of Inverse Reinforcement Learning. [pdf]
  • Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli. ICML 2023.
• Identifiability and Generalizability in Constrained Inverse Reinforcement Learning. [pdf]
  • Andreas Schlaginhaufen, Maryam Kamgarpour. ICML 2023.
• Inverse Reinforcement Learning without Reinforcement Learning. [pdf]
  • Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu. ICML 2023.
• Inverse Reinforcement Learning Framework for Transferring Task Sequencing Policies from Humans to Robots in Manufacturing Applications. [pdf]
  • Omey M. Manyar, Zachary McNulty, Stefanos Nikolaidis, Satyandra K. Gupta. ICRA 2023.
• Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation. [pdf]
  • Samuel Triest, Mateo Guaman Castro, Parv Maheshwari, Matthew Sivaprakasam, Wenshan Wang, Sebastian A. Scherer. ICRA 2023.
• DriveIRL: Drive in Real Life with Inverse Reinforcement Learning. [pdf]
  • Tung Phan-Minh, Forbes Howington, Ting-Sheng Chu, Momchil S. Tomov, Robert E. Beaudoin, Sang Uk Lee, Nanxiang Li, Caglayan Dicle, Samuel Findler, Francisco Suárez-Ruiz, Bo Yang, Sammy Omari, Eric M. Wolff. ICRA 2023.
• Show me What you want: Inverse Reinforcement Learning to Automatically Design Robot Swarms by Demonstration. [pdf]
  • Ilyes Gharbi, Jonas Kuckling, David Garzón-Ramos, Mauro Birattari. ICRA 2023.
• Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control. [pdf]
  • Jiayu Chen, Tian Lan, Vaneet Aggarwal. ICRA 2023.
• SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning. [pdf]
  • Yifan Xu, Theodor Chakhachiro, Tribhi Kathuria, Maani Ghaffari. ICRA 2023.
• InitLight: Initial Model Generation for Traffic Signal Control Using Adversarial Inverse Reinforcement Learning. [pdf]
  • Yutong Ye, Yingbo Zhou, Jiepin Ding, Ting Wang, Mingsong Chen, Xiang Lian. IJCAI 2023.

• Deep Reinforcement Learning from Human Preferences. [pdf]
  • Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, Dario Amodei NeurIPS 2017.
• Training Language Models to Follow Instructions with Human Feedback. [pdf]
  • Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, Ryan Lowe. NeurIPS 2022.
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model. [pdf]
  • Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn. NeurIPS 2023.
• Guiding Pretraining in Reinforcement Learning with Large Language Models. [pdf]
  • Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas. ICML 2023.
• Reward Design with Language Models. [pdf]
  • Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh. ICLR 2023.
• Pre-Trained Language Models for Interactive Decision-Making. [pdf]
  • Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu. NeurIPS 2022.

If you use this toolbox in your research, please cite this project.

@misc{YalunAwesome,
    author = {Yalun Wu},
    title = {Reinforcement-Learning-Papers},
    year = {2023},
    howpublished = {\url{https://github.com/Allenpandas/Reinforcement-Learning-Papers}}
}