Reinforcement-Learning-Papers

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.

News

• 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.

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

• 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

1_Multi-Agent Reinforcement Learning

• 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,