README.zh-CN.md

欢迎来到我们的GitHub仓库！这个仓库致力于记录 强化学习 领域在顶级学术会议，如：AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS 等录用的重要研究论文。我们为您提供了一个便捷的资源库，以帮助您跟踪最新的强化学习进展，深入了解领域内的研究趋势，并探讨最前沿的算法和方法。

新闻

• 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.

参与贡献

Markdown format：

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

请通过联系我或 添加请求 来帮助贡献此列表。

如有任何问题，请随时与我联系 📮.

Table of Contents

• 1_多智能体强化学习 (MARL)
• 2_元强化学习 (Meta RL)
• 3_分层强化学习 (HRL)
• 4_多任务强化学习 (Multi-Task RL)
• 5_离线强化学习 (Offline RL)
• 6_逆强化学习 (IRL)

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, 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.

2_Meta Reinforcement Learning

• 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.

3_Hierarchical Reinforcement Learning

• 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.

4_Multi-Task Rinforcement Learning

• 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.

5_Offline Reinforcement Learning

• 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).

6_Inverse Reinforcement Learning

• 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.

引用

如果你在你的研究中使用这个工具箱，请引用这个项目。

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