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.
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 📮.
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]
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]
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]
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]
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]
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.
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}}
}