The proceedings of top conference in 2023 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.

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2021-Reinforcement-Learning-Conferences-Papers
2020-Reinforcement-Learning-Conferences-Papers
2019-Reinforcement-Learning-Conferences-Papers
2018-Reinforcement-Learning-Conferences-Papers
2017-Reinforcement-Learning-Conferences-Papers

• Related repository
• AAAI'2023
• AAMAS'2023
• ICLR'2023
• ICML'2023
• ICRA'2023
• IJCAI'2023
• NeurIPS'2023

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 📮.

• AAAI Conference on Artificial Intelligence (AAAI'2023)
• International Conference on Autonomous Agents and Multiagent Systems (AAMAS'2023)
• International Conference on Learning Representations (ICLR'2023)
• International Conference on Machine Learning (ICML'2023)
• International Conference on Robotics and Automation (ICRA'2023)
• International Joint Conference on Artificial Intelligence (IJCAI'2023)

• Siamese-Discriminant Deep Reinforcement Learning for Solving Jigsaw Puzzles with Large Eroded Gaps. [pdf]
  • Xingke Song, Jiahuan Jin, Chenglin Yao, Shihe Wang, Jianfeng Ren, Ruibin Bai. AAAI 2023.
• RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning. [pdf]
  • Boxuan Zhao, Jun Zhang, Deheng Ye, Jian Cao, Xiao Han, Qiang Fu, Wei Yang. AAAI 2023.
• Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories. [pdf]
  • Christopher W. F. Parsonson, Alexandre Laterre, Thomas D. Barrett. AAAI 2023.
• End-to-End Entity Linking with Hierarchical Reinforcement Learning. [pdf]
  • Lihan Chen, Tinghui Zhu, Jingping Liu, Jiaqing Liang, Yanghua Xiao. AAAI 2023.
• Let Graph Be the Go Board: Gradient-Free Node Injection Attack for Graph Neural Networks via Reinforcement Learning. [pdf]
  • Mingxuan Ju, Yujie Fan, Chuxu Zhang, Yanfang Ye. AAAI 2023.
• AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems. [pdf]
  • Xu Wan, Mingyang Sun, Boli Chen, Zhongda Chu, Fei Teng. AAAI 2023.
• An Efficient Deep Reinforcement Learning Algorithm for Solving Imperfect Information Extensive-Form Games. [pdf]
  • Linjian Meng, Zhenxing Ge, Pinzhuo Tian, Bo An, Yang Gao. AAAI 2023.
• Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning. [pdf]
  • Jacob Adamczyk, Argenis Arriojas, Stas Tiomkin, Rahul V. Kulkarni. AAAI 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.
• Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm. [pdf]
  • Qinbo Bai, Amrit Singh Bedi, Vaneet Aggarwal. AAAI 2023.
• RePreM: Representation Pre-training with Masked Model for Reinforcement Learning. [pdf]
  • Yuanying Cai, Chuheng Zhang, Wei Shen, Xuyun Zhang, Wenjie Ruan, Longbo Huang. AAAI 2023.
• Learning Pessimism for Reinforcement Learning. [pdf]
  • Edoardo Cetin, Oya Çeliktutan. AAAI 2023.
• Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning. [pdf]
  • Souradip Chakraborty, Amrit Singh Bedi, Pratap Tokekar, Alec Koppel, Brian M. Sadler, Furong Huang, Dinesh Manocha. AAAI 2023.
• Offline Quantum Reinforcement Learning in a Conservative Manner. [pdf]
  • Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao. AAAI 2023.
• Augmented Proximal Policy Optimization for Safe Reinforcement Learning. [pdf]
  • Juntao Dai, Jiaming Ji, Long Yang, Qian Zheng, Gang Pan. AAAI 2023.
• Incremental Reinforcement Learning with Dual-Adaptive ε-Greedy Exploration. [pdf]
  • Wei Ding, Siyang Jiang, Hsi-Wen Chen, Ming-Syan Chen. AAAI 2023.
• Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints. [pdf]
  • Yuhao Ding, Javad Lavaei. AAAI 2023.
• Non-stationary Risk-Sensitive Reinforcement Learning: Near-Optimal Dynamic Regret, Adaptive Detection, and Separation Design. [pdf]
  • Yuhao Ding, Ming Jin, Javad Lavaei. AAAI 2023.
• Model-Based Offline Reinforcement Learning with Local Misspecification. [pdf]
  • Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill. AAAI 2023.
• Fast Counterfactual Inference for History-Based Reinforcement Learning. [pdf]
  • Haichuan Gao, Tianren Zhang, Zhile Yang, Yuqing Guo, Jinsheng Ren, Shangqi Guo, Feng Chen. AAAI 2023.
• Dream to Generalize: Zero-Shot Model-Based Reinforcement Learning for Unseen Visual Distractions. [pdf]
  • Jeongsoo Ha, Kyungsoo Kim, Yusung Kim. AAAI 2023.
• Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation. [pdf]
  • Taehyun Hwang, Min-hwan Oh. AAAI 2023.
• Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning. [pdf]
  • Jiechuan Jiang, Zongqing Lu. AAAI 2023.
• On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples. [pdf]
  • Mustafa O. Karabag, Ufuk Topcu. AAAI 2023.
• Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness. [pdf]
  • Ezgi Korkmaz. AAAI 2023.
• FanoutNet: A Neuralized PCB Fanout Automation Method Using Deep Reinforcement Learning. [pdf]
  • Haiyun Li, Jixin Zhang, Ning Xu, Mingyu Liu. AAAI 2023.
• SplitNet: A Reinforcement Learning Based Sequence Splitting Method for the MinMax Multiple Travelling Salesman Problem. [pdf]
  • Hebin Liang, Yi Ma, Zilin Cao, Tianyang Liu, Fei Ni, Zhigang Li, Jianye Hao. AAAI 2023.
• Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning. [pdf]
  • Weijian Liao, Zongzhang Zhang, Yang Yu. AAAI 2023.
• Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning. [pdf]
  • Bo Liu, Yihao Feng, Qiang Liu, Peter Stone. AAAI 2023.
• Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions. [pdf]
  • Qiyuan Liu, Qi Zhou, Rui Yang, Jie Wang. AAAI 2023.
• Local Explanations for Reinforcement Learning. [pdf]
  • Ronny Luss, Amit Dhurandhar, Miao Liu. AAAI 2023.
• Online Reinforcement Learning with Uncertain Episode Lengths. [pdf]
  • Debmalya Mandal, Goran Radanovic, Jiarui Gan, Adish Singla, Rupak Majumdar. AAAI 2023.
• Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. [pdf]
  • Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli. AAAI 2023.
• On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation. [pdf]
  • Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora. AAAI 2023.
• Conceptual Reinforcement Learning for Language-Conditioned Tasks. [pdf]
  • Shaohui Peng, Xing Hu, Rui Zhang, Jiaming Guo, Qi Yi, Ruizhi Chen, Zidong Du, Ling Li, Qi Guo, Yunji Chen. AAAI 2023.
• Weighted Policy Constraints for Offline Reinforcement Learning. [pdf]
  • Zhiyong Peng, Changlin Han, Yadong Liu, Zongtan Zhou. AAAI 2023.
• Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes. [pdf]
  • Chao Qu, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi, James Zhang, Hongyuan Mei. AAAI 2023.
• Hypernetworks for Zero-Shot Transfer in Reinforcement Learning. [pdf]
  • Sahand Rezaei-Shoshtari, Charlotte Morissette, François Robert Hogan, Gregory Dudek, David Meger. AAAI 2023.
• Simultaneously Updating All Persistence Values in Reinforcement Learning. [pdf]
  • Luca Sabbioni, Luca Al Daire, Lorenzo Bisi, Alberto Maria Metelli, Marcello Restelli. AAAI 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.
• Efficient Exploration in Resource-Restricted Reinforcement Learning. [pdf]
  • Zhihai Wang, Taoxing Pan, Qi Zhou, Jie Wang. 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.
• Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery. [pdf]
  • Yiqin Yang, Hao Hu, Wenzhe Li, Siyuan Li, Jun Yang, Qianchuan Zhao, Chongjie Zhang. 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.
• Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning. [pdf]
  • Yang Yue, Bingyi Kang, Zhongwen Xu, Gao Huang, Shuicheng Yan. AAAI 2023.
• Quantum Multi-Agent Meta Reinforcement Learning. [pdf]
  • Won Joon Yun, Jihong Park, Joongheon Kim. AAAI 2023.
• Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning. [pdf]
  • Guoxi Zhang, Hisashi Kashima. AAAI 2023.
• DARL: Distance-Aware Uncertainty Estimation for Offline Reinforcement Learning. [pdf]
  • Hongchang Zhang, Jianzhun Shao, Shuncheng He, Yuhang Jiang, Xiangyang Ji. AAAI 2023.
• Adaptive Policy Learning for Offline-to-Online Reinforcement Learning. [pdf]
  • Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, Jing Jiang. AAAI 2023.
• Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning. [pdf]
  • Zixian Zhou, Mengda Huang, Feiyang Pan, Jia He, Xiang Ao, Dandan Tu, Qing He. 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.
• Hierarchical Mean-Field Deep Reinforcement Learning for Large-Scale Multiagent Systems. [pdf]
  • Chao Yu. AAAI 2023.
• DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning. [pdf]
  • Tingting Yuan, Hwei-Ming Chung, Jie Yuan, Xiaoming Fu. AAAI 2023.
• A Dynamics and Task Decoupled Reinforcement Learning Architecture for High-Efficiency Dynamic Target Intercept. [pdf]
  • Dora D. Liu, Liang Hu, Qi Zhang, Tangwei Ye, Usman Naseem, Zhong Yuan Lai. AAAI 2023.
• Large-State Reinforcement Learning for Hyper-Heuristics. [pdf]
  • Lucas Kletzander, Nysret Musliu. AAAI 2023.
• End-to-End Deep Reinforcement Learning for Conversation Disentanglement. [pdf]
  • Karan Bhukar, Harshit Kumar, Dinesh Raghu, Ajay Gupta. AAAI 2023.
• Leveraging Modality-Specific Representations for Audio-Visual Speech Recognition via Reinforcement Learning. [pdf]
  • Chen Chen, Yuchen Hu, Qiang Zhang, Heqing Zou, Beier Zhu, Eng Siong Chng. AAAI 2023.
• Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation. [pdf]
  • Wenqing Chen, Jidong Tian, Caoyun Fan, Yitian Li, Hao He, Yaohui Jin. AAAI 2023.
• On the Challenges of Using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects. [pdf]
  • Sumana Basu, Marc-André Legault, Adriana Romero-Soriano, Doina Precup. AAAI 2023.
• Critical Firms Prediction for Stemming Contagion Risk in Networked-Loans through Graph-Based Deep Reinforcement Learning. [pdf]
  • Dawei Cheng, Zhibin Niu, Jianfu Zhang, Yiyi Zhang, Changjun Jiang. AAAI 2023.
• Low Emission Building Control with Zero-Shot Reinforcement Learning. [pdf]
  • Scott R. Jeen, Alessandro Abate, Jonathan M. Cullen. AAAI 2023.
• Safe Reinforcement Learning via Shielding under Partial Observability. [pdf]
  • Steven Carr, Nils Jansen, Sebastian Junges, Ufuk Topcu. AAAI 2023.
• PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks. [pdf]
  • Anandsingh Chauhan, Mayank Baranwal, Ansuma Basumatary. AAAI 2023.
• Correct-by-Construction Reinforcement Learning of Cardiac Pacemakers from Duration Calculus Requirements. [pdf]
  • Kalyani Dole, Ashutosh Gupta, John Komp, Shankaranarayanan Krishna, Ashutosh Trivedi. AAAI 2023.
• SafeLight: A Reinforcement Learning Method toward Collision-Free Traffic Signal Control. [pdf]
  • Wenlu Du, Junyi Ye, Jingyi Gu, Jing Li, Hua Wei, Guiling Wang. AAAI 2023.
• AutoCost: Evolving Intrinsic Cost for Zero-Violation Reinforcement Learning. [pdf]
  • Tairan He, Weiye Zhao, Changliu Liu. 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.
• Constrained Reinforcement Learning in Hard Exploration Problems. [pdf]
  • Pathmanathan Pankayaraj, Pradeep Varakantham. AAAI 2023.
• STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning. [pdf]
  • Nikhil Kumar Singh, Indranil Saha. AAAI 2023.
• Misspecification in Inverse Reinforcement Learning. [pdf]
  • Joar Skalse, Alessandro Abate. AAAI 2023.
• User-Oriented Robust Reinforcement Learning. [pdf]
  • Haoyi You, Beichen Yu, Haiming Jin, Zhaoxing Yang, Jiahui Sun. AAAI 2023.
• Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks. [pdf]
  • Linrui Zhang, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang, Dacheng Tao. AAAI 2023.
• Vessel-to-Vessel Motion Compensation with Reinforcement Learning. [pdf]
  • Sverre Herland, Kerstin Bach. AAAI 2023.
• Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning. [pdf]
  • Flemming Kondrup, Thomas Jiralerspong, Elaine Lau, Nathan de Lara, Jacob Shkrob, My Duc Tran, Doina Precup, Sumana Basu. AAAI 2023.
• Reward Design for an Online Reinforcement Learning Algorithm Supporting Oral Self-Care. [pdf]
  • Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy. AAAI 2023.
• Non-exponential Reward Discounting in Reinforcement Learning. [pdf]
  • Raja Farrukh Ali. AAAI 2023.
• Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning With Applications to Ridesharing. [pdf]
  • Lucia Cipolina-Kun. AAAI 2023.
• Multi-Horizon Learning in Procedurally-Generated Environments for Off-Policy Reinforcement Learning (Student Abstract). [pdf]
  • Raja Farrukh Ali, Kevin Duong, Nasik Muhammad Nafi, William H. Hsu. AAAI 2023.
• Know Your Enemy: Identifying Adversarial Behaviours in Deep Reinforcement Learning Agents (Student Abstract). [pdf]
  • Seán Caulfield Curley, Karl Mason, Patrick Mannion. AAAI 2023.
• Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract). [pdf]
  • Chao Chen, Dawei Wang, Feng Mao, Zongzhang Zhang, Yang Yu. AAAI 2023.
• Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract). [pdf]
  • Briti Gangopadhyay, Pallab Dasgupta, Soumyajit Dey. AAAI 2023.
• Towards Safe Reinforcement Learning via OOD Dynamics Detection in Autonomous Driving System (Student Abstract). [pdf]
  • Arnaud Gardille, Ola Ahmad. AAAI 2023.
• A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract). [pdf]
  • Li-Chun Huang, Nai-Zen Hseuh, Yen-Che Chien, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng. AAAI 2023.
• Anti-drifting Feature Selection via Deep Reinforcement Learning (Student Abstract). [pdf]
  • Aoran Wang, Hongyang Yang, Feng Mao, Zongzhang Zhang, Yang Yu, Xiaoyang Liu. 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.
• SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning. [pdf]
  • Réda Alami, Hakim Hacid, Lorenzo Bellone, Michal Barcis, Enrico Natalizio. 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.
• The Benefits of Power Regularization in Cooperative Reinforcement Learning. [pdf]
  • Michelle Li, Michael Dennis. AAMAS 2023.
• Sequential Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Yifan Zang, Jinmin He, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing. AAMAS 2023.
• Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning. [pdf]
  • Haochen Wu, Pedro Sequeira, David V. Pynadath. AAMAS 2023.
• Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning. [pdf]
  • Durgesh Kalwar, Omkar Shelke, Somjit Nath, Hardik Meisheri, Harshad Khadilkar. AAMAS 2023.
• FedFormer: Contextual Federation with Attention in Reinforcement Learning. [pdf]
  • Liam Hebert, Lukasz Golab, Pascal Poupart, Robin Cohen. AAMAS 2023.
• Enhancing Reinforcement Learning Agents with Local Guides. [pdf]
  • Paul Daoudi, Bogdan Robu, Christophe Prieur, Ludovic Dos Santos, Merwan Barlier. AAMAS 2023.
• Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models. [pdf]
  • Tom Haider, Karsten Roscher, Felippe Schmoeller da Roza, Stephan Günnemann. AAMAS 2023.
• Knowledge Compilation for Constrained Combinatorial Action Spaces in Reinforcement Learning. [pdf]
  • Jiajing Ling, Moritz Lukas Schuler, Akshat Kumar, Pradeep Varakantham. AAMAS 2023.
• Learn to Solve the Min-max Multiple Traveling Salesmen Problem with Reinforcement Learning. [pdf]
  • Junyoung Park, Changhyun Kwon, Jinkyoo Park. AAMAS 2023.
• A Hybrid Framework of Reinforcement Learning and Physics-Informed Deep Learning for Spatiotemporal Mean Field Games. [pdf]
  • Xu Chen, Shuo Liu, Xuan Di. AAMAS 2023.
• Adversarial Inverse Reinforcement Learning for Mean Field Games. [pdf]
  • Yang Chen, Libo Zhang, Jiamou Liu, Michael Witbrock. AAMAS 2023.
• GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations. [pdf]
  • Tobias Huber, Maximilian Demmler, Silvan Mertes, Matthew L. Olson, Elisabeth André. 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.
• Curriculum Offline Reinforcement Learning. [pdf]
  • Yuanying Cai, Chuheng Zhang, Hanye Zhao, Li Zhao, Jiang Bian. AAMAS 2023.
• Decentralized Model-Free Reinforcement Learning in Stochastic Games with Average-Reward Objective. [pdf]
  • Romain Cravic, Nicolas Gast, Bruno Gaujal. AAMAS 2023.
• Less Is More: Refining Datasets for Offline Reinforcement Learning with Reward Machines. [pdf]
  • Haoyuan Sun, Feng Wu. AAMAS 2023.
• D-Shape: Demonstration-Shaped Reinforcement Learning via Goal-Conditioning. [pdf]
  • Caroline Wang, Garrett Warnell, Peter Stone. AAMAS 2023.
• Safe Deep Reinforcement Learning by Verifying Task-Level Properties. [pdf]
  • Enrico Marchesini, Luca Marzari, Alessandro Farinelli, Christopher Amato. AAMAS 2023.
• Heterogeneous Multi-Robot Reinforcement Learning. [pdf]
  • Matteo Bettini, Ajay Shankar, Amanda Prorok. 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 Reinforcement Learning for Auto-bidding in Display Advertising. [pdf]
  • Shuang Chen, Qisen Xu, Liang Zhang, Yongbo Jin, Wenhao Li, Linjian Mo. 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.
• Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks. [pdf]
  • Mohammad Mohammadi, Jonathan Nöther, Debmalya Mandal, Adish Singla, Goran Radanovic. AAMAS 2023.
• Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. [pdf]
  • Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu. AAMAS 2023.
• Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning. [pdf]
  • Woojun Kim, Youngchul Sung. AAMAS 2023.
• Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning. [pdf]
  • Junqi Qian, Paul Weng, Chenmien Tan. AAMAS 2023.
• A Deep Reinforcement Learning Approach for Online Parcel Assignment. [pdf]
  • Hao Zeng, Qiong Wu, Kunpeng Han, Junying He, Haoyuan Hu. AAMAS 2023.
• A Brief Guide to Multi-Objective Reinforcement Learning and Planning. [pdf]
  • Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers. AAMAS 2023.
• Welfare and Fairness in Multi-objective Reinforcement Learning. [pdf]
  • Zimeng Fan, Nianli Peng, Muhang Tian, Brandon Fain. AAMAS 2023.
• Hierarchical Reinforcement Learning with Human-AI Collaborative Sub-Goals Optimization. [pdf]
  • Haozhe Ma, Thanh Vinh Vo, Tze-Yun Leong. 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.
• Learning Constraints From Human Stop-Feedback in Reinforcement Learning. [pdf]
  • Silvia Poletti, Alberto Testolin, Sebastian Tschiatschek. AAMAS 2023.
• Hierarchical Reinforcement Learning for Ad Hoc Teaming. [pdf]
  • Stéphane Aroca-Ouellette, Miguel Aroca-Ouellette, Upasana Biswas, Katharina Kann, Alessandro Roncone. AAMAS 2023.
• Multi-Agent Deep Reinforcement Learning for High-Frequency Multi-Market Making. [pdf]
  • Pankaj Kumar. AAMAS 2023.
• TA-Explore: Teacher-Assisted Exploration for Facilitating Fast Reinforcement Learning. [pdf]
  • Ali Beikmohammadi, Sindri Magnússon. 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.
• AJAR: An Argumentation-based Judging Agents Framework for Ethical Reinforcement Learning. [pdf]
  • Benoît Alcaraz, Olivier Boissier, Rémy Chaput, Christopher Leturc. AAMAS 2023.
• Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning. [pdf]
  • Pranav Khanna, Guy Tennenholtz, Nadav Merlis, Shie Mannor, Chen Tessler. 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.
• Search-Improved Game-Theoretic Multiagent Reinforcement Learning in General and Negotiation Games. [pdf]
  • Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Kate Larson, Yoram Bachrach, Michael P. Wellman, Paul Muller. AAMAS 2023.
• Grey-box Adversarial Attack on Communication in Multi-agent Reinforcement Learning. [pdf]
  • Xiao Ma, Wu-Jun Li. AAMAS 2023.
• Reward-Machine-Guided, Self-Paced Reinforcement Learning. [pdf]
  • Cevahir Köprülü, Ufuk Topcu. AAMAS 2023.
• Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning. [pdf]
  • Chaitanya Kharyal, Tanmay Sinha, Sai Krishna Gottipati, Fatemeh Abdollahi, Srijita Das, Matthew E. Taylor. AAMAS 2023.
• PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning. [pdf]
  • Jizhou Wu, Tianpei Yang, Xiaotian Hao, Jianye Hao, Yan Zheng, Weixun Wang, Matthew E. Taylor. 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.
• Optimizing Crop Management with Reinforcement Learning and Imitation Learning. [pdf]
  • Ran Tao, Pan Zhao, Jing Wu, Nicolas F. Martin, Matthew T. Harrison, Carla Sofia Santos Ferreira, Zahra Kalantari, Naira Hovakimyan. AAMAS 2023.
• Balancing Fairness and Efficiency in Transport Network Design through Reinforcement Learning. [pdf]
  • Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos. 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 to Perceive in Deep Model-Free Reinforcement Learning. [pdf]
  • Gonçalo Querido, Alberto Sardinha, Francisco S. Melo. AAMAS 2023.
• Analyzing the Sensitivity to Policy-Value Decoupling in Deep Reinforcement Learning Generalization. [pdf]
  • Nasik Muhammad Nafi, Raja Farrukh Ali, William H. Hsu. AAMAS 2023.
• Reinforcement Learning with Depreciating Assets. [pdf]
  • Taylor Dohmen, Ashutosh Trivedi. 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.
• 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.
• Hierarchical Reinforcement Learning with Attention Reward. [pdf]
  • Sihong Luo, Jinghao Chen, Zheng Hu, Chunhong Zhang, Benhui Zhuang. AAMAS 2023.
• Know Your Enemy: Identifying and Adapting to Adversarial Attacks in Deep Reinforcement Learning. [pdf]
  • Seán Caulfield Curley, Karl Mason, Patrick Mannion. AAMAS 2023.
• Transformer Actor-Critic with Regularization: Automated Stock Trading using Reinforcement Learning. [pdf]
  • Namyeong Lee, Jun Moon. AAMAS 2023.
• Model-Based Actor-Critic for Multi-Objective Reinforcement Learning with Dynamic Utility Functions. [pdf]
  • Johan Källström, Fredrik Heintz. AAMAS 2023.
• Relaxed Exploration Constrained Reinforcement Learning. [pdf]
  • Shahaf S. Shperberg, Bo Liu, Peter Stone. AAMAS 2023.
• Causality Detection for Efficient Multi-Agent Reinforcement Learning. [pdf]
  • Rafael Pina, Varuna De Silva, Corentin Artaud. AAMAS 2023.
• Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition. [pdf]
  • Peter Sunehag, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Igor Mordatch, Joel Z. Leibo. AAMAS 2023.
• Multi-objective Reinforcement Learning in Factored MDPs with Graph Neural Networks. [pdf]
  • Marc Vincent, Amal El Fallah Seghrouchni, Vincent Corruble, Narayan Bernardin, Rami Kassab, Frédéric Barbaresco. 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.
• LTL-Based Non-Markovian Inverse Reinforcement Learning. [pdf]
  • Mohammad Afzal, Sankalp Gambhir, Ashutosh Gupta, S. Krishna, Ashutosh Trivedi, Alvaro Velasquez. AAMAS 2023.
• Counterfactual Explanations for Reinforcement Learning Agents. [pdf]
  • Jasmina Gajcin. AAMAS 2023.
• Enhancing User Understanding of Reinforcement Learning Agents Through Visual Explanations. [pdf]
  • Yotam Amitai. AAMAS 2023.
• Towards Sample-Efficient Multi-Objective Reinforcement Learning. [pdf]
  • Lucas Nunes Alegre. AAMAS 2023.
• Reinforcement Learning and Mechanism Design for Routing of Connected and Autonomous Vehicles. [pdf]
  • Behrad Koohy. 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.
• Learning Representations and Robust Exploration for Improved Generalization in Reinforcement Learning. [pdf]
  • Nasik Muhammad Nafi. AAMAS 2023.
• Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning. [pdf]
  • Lucia Cipolina-Kun. AAMAS 2023.
• Improvement and Evaluation of the Policy Legibility in Reinforcement Learning. [pdf]
  • Yanyu Liu, Yifeng Zeng, Biyang Ma, Yinghui Pan, Huifan Gao, Xiaohan Huang. AAMAS 2023.

• The Role of Coverage in Online Reinforcement Learning. [pdf]
  • Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade. ICLR 2023.
• Confidence-Conditioned Value Functions for Offline Reinforcement Learning. [pdf]
  • Joey Hong, Aviral Kumar, Sergey Levine. ICLR 2023.
• In-context Reinforcement Learning with Algorithm Distillation. [pdf]
  • Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan A. Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih. ICLR 2023.
• Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning. [pdf]
  • Anton Bakhtin, David J. Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina, Alexander H. Miller, Noam Brown. ICLR 2023.
• Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. [pdf]
  • Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville. ICLR 2023.
• Near-optimal Policy Identification in Active Reinforcement Learning. [pdf]
  • Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic. ICLR 2023.
• Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. [pdf]
  • Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot. ICLR 2023.
• The In-Sample Softmax for Offline Reinforcement Learning. [pdf]
  • Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White. ICLR 2023.
• Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization. [pdf]
  • Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, Yejin Choi. ICLR 2023.
• TEMPERA: Test-Time Prompt Editing via Reinforcement Learning. [pdf]
  • Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez. ICLR 2023.
• Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes. [pdf]
  • Eoin M. Kenny, Mycal Tucker, Julie Shah. ICLR 2023.
• A CMDP-within-online framework for Meta-Safe Reinforcement Learning. [pdf]
  • Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei, Ming Jin. ICLR 2023.
• Does Zero-Shot Reinforcement Learning Exist? [pdf]
  • Ahmed Touati, Jérémy Rapin, Yann Ollivier. ICLR 2023.
• Hyperbolic Deep Reinforcement Learning. [pdf]
  • Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt. ICLR 2023.
• Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning. [pdf]
  • Onno Eberhard, Jakob Hollenstein, Cristina Pinneri, Georg Martius. ICLR 2023.
• RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch. [pdf]
  • Yiqin Tan, Pihe Hu, Ling Pan, Jiatai Huang, Longbo Huang. ICLR 2023.
• DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems. [pdf]
  • Pierre Schumacher, Daniel F. B. Haeufle, Dieter Büchler, Syn Schmitt, Georg Martius. ICLR 2023.
• A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. [pdf]
  • Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan. ICLR 2023.
• Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. [pdf]
  • Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius. ICLR 2023.
• Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation. [pdf]
  • Daesol Cho, Seungjae Lee, H. Jin Kim. ICLR 2023.
• Near-Optimal Adversarial Reinforcement Learning with Switching Costs. [pdf]
  • Ming Shi, Yingbin Liang, Ness B. Shroff. ICLR 2023.
• LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. [pdf]
  • Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters. ICLR 2023.
• Gray-Box Gaussian Processes for Automated Reinforcement Learning. [pdf]
  • Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka. ICLR 2023.
• Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation. [pdf]
  • Yannick Hogewind, Thiago D. Simão, Tal Kachman, Nils Jansen. ICLR 2023.
• Efficient Deep Reinforcement Learning Requires Regulating Overfitting. [pdf]
  • Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine. ICLR 2023.
• Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. [pdf]
  • Ming Yin, Mengdi Wang, Yu-Xiang Wang. ICLR 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.
• 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.
• 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.
• PAC Reinforcement Learning for Predictive State Representations. [pdf]
  • Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee. ICLR 2023.
• Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning. [pdf]
  • Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah P. Hanna, Stefano V. Albrecht. ICLR 2023.
• Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning. [pdf]
  • Zhendong Wang, Jonathan J. Hunt, Mingyuan Zhou. ICLR 2023.
• Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling. [pdf]
  • Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu. ICLR 2023.
• Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning. [pdf]
  • Deyao Zhu, Li Erran Li, Mohamed Elhoseiny. ICLR 2023.
• User-Interactive Offline Reinforcement Learning. [pdf]
  • Phillip Swazinna, Steffen Udluft, Thomas A. Runkler. 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.
• Quality-Similar Diversity via Population Based Reinforcement Learning. [pdf]
  • Shuang Wu, Jian Yao, Haobo Fu, Ye Tian, Chao Qian, Yaodong Yang, Qiang Fu, Wei Yang. ICLR 2023.
• When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning. [pdf]
  • Jianxiong Li, Xianyuan Zhan, Haoran Xu, Xiangyu Zhu, Jingjing Liu, Ya-Qin Zhang. ICLR 2023.
• Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. [pdf]
  • Dan Qiao, Yu-Xiang Wang. ICLR 2023.
• MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. [pdf]
  • Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran. ICLR 2023.
• PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm. [pdf]
  • Toygun Basaklar, Suat Gumussoy, Ümit Y. Ogras. ICLR 2023.
• Latent Variable Representation for Reinforcement Learning. [pdf]
  • Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai. ICLR 2023.
• Spectral Decomposition Representation for Reinforcement Learning. [pdf]
  • Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai. ICLR 2023.
• Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs. [pdf]
  • Yuan Cheng, Ruiquan Huang, Yingbin Liang, Jing Yang. ICLR 2023.
• Provably Efficient Lifelong Reinforcement Learning with Linear Representation. [pdf]
  • Sanae Amani, Lin Yang, Ching-An Cheng. ICLR 2023.
• Causal Imitation Learning via Inverse Reinforcement Learning. [pdf]
  • Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim. ICLR 2023.
• A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. [pdf]
  • Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer. ICLR 2023.
• Scaling Laws for a Multi-Agent Reinforcement Learning Model. [pdf]
  • Oren Neumann, Claudius Gros. ICLR 2023.
• Towards Minimax Optimal Reward-free Reinforcement Learning in Linear MDPs. [pdf]
  • Pihe Hu, Yu Chen, Longbo Huang. ICLR 2023.
• On the Data-Efficiency with Contrastive Image Transformation in Reinforcement Learning. [pdf]
  • Sicong Liu, Xi Sheryl Zhang, Yushuo Li, Yifan Zhang, Jian Cheng. ICLR 2023.
• Quasi-optimal Reinforcement Learning with Continuous Actions. [pdf]
  • Yuhan Li, Wenzhuo Zhou, Ruoqing Zhu. ICLR 2023.
• Policy Expansion for Bridging Offline-to-Online Reinforcement Learning. [pdf]
  • Haichao Zhang, Wei Xu, Haonan Yu. ICLR 2023.
• Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning. [pdf]
  • Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters. ICLR 2023.
• On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning. [pdf]
  • Yifan Xu, Nicklas Hansen, Zirui Wang, Yung-Chieh Chan, Hao Su, Zhuowen Tu. ICLR 2023.
• Benchmarking Constraint Inference in Inverse Reinforcement Learning. [pdf]
  • Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart. ICLR 2023.
• Distributional Meta-Gradient Reinforcement Learning. [pdf]
  • Haiyan Yin, Shuicheng Yan, Zhongwen Xu. ICLR 2023.
• In-sample Actor Critic for Offline Reinforcement Learning. [pdf]
  • Hongchang Zhang, Yixiu Mao, Boyuan Wang, Shuncheng He, Yi Xu, Xiangyang Ji. ICLR 2023.
• EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model. [pdf]
  • Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Jinyi Liu, Yingfeng Chen, Changjie Fan. ICLR 2023.
• Boosting Multiagent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks. [pdf]
  • Jianye Hao, Xiaotian Hao, Hangyu Mao, Weixun Wang, Yaodong Yang, Dong Li, Yan Zheng, Zhen Wang. ICLR 2023.
• Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. [pdf]
  • Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche. 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.
• Behavior Prior Representation learning for Offline Reinforcement Learning. [pdf]
  • Hongyu Zang, Xin Li, Jie Yu, Chen Liu, Riashat Islam, Remi Tachet des Combes, Romain Laroche. ICLR 2023.
• Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. [pdf]
  • Haoye Lu, Daniel Herman, Yaoliang Yu. ICLR 2023.
• The Provable Benefit of Unsupervised Data Sharing for Offline Reinforcement Learning. [pdf]
  • Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang. ICLR 2023.
• Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. [pdf]
  • Thanh Lam, Arun Verma, Bryan Kian Hsiang Low, Patrick Jaillet. 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.
• On the Robustness of Safe Reinforcement Learning under Observational Perturbations. [pdf]
  • Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, Ding Zhao. ICLR 2023.
• Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes. [pdf]
  • Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang. 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.
• Deep Reinforcement Learning for Cost-Effective Medical Diagnosis. [pdf]
  • Zheng Yu, Yikuan Li, Joseph Kim, Kaixuan Huang, Yuan Luo, Mengdi Wang. ICLR 2023.
• POPGym: Benchmarking Partially Observable Reinforcement Learning. [pdf]
  • Steven D. Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, Amanda Prorok. ICLR 2023.
• Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning. [pdf]
  • Sasha Salter, Kristian Hartikainen, Walter Goodwin, Ingmar Posner. ICLR 2023.
• ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation. [pdf]
  • Jianye Hao, Pengyi Li, Hongyao Tang, Yan Zheng, Xian Fu, Zhaopeng Meng. ICLR 2023.
• Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path. [pdf]
  • Yihan Du, Siwei Wang, Longbo Huang. ICLR 2023.
• Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. [pdf]
  • Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang. ICLR 2023.
• Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. [pdf]
  • Chang Liu, Zetian Jiang, Runzhong Wang, Lingxiao Huang, Pinyan Lu, Junchi Yan. ICLR 2023.
• Order Matters: Agent-by-agent Policy Optimization. [pdf]
  • Xihuai Wang, Zheng Tian, Ziyu Wan, Ying Wen, Jun Wang, Weinan Zhang. ICLR 2023.

• Efficient Online Reinforcement Learning with Offline Data. [pdf]
  • Philip J. Ball, Laura M. Smith, Ilya Kostrikov, Sergey Levine. ICML 2023.
• Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. [pdf]
  • Anas Barakat, Ilyas Fatkhullin, Niao He. ICML 2023.
• Explaining Reinforcement Learning with Shapley Values. [pdf]
  • Daniel Beechey, Thomas M. S. Smith, Özgür Simsek. ICML 2023.
• StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes. [pdf]
  • Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan. ICML 2023.
• The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning. [pdf]
  • Victor Boone, Bruno Gaujal. ICML 2023.
• Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning. [pdf]
  • Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, Pierre-Yves Oudeyer. ICML 2023.
• Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning. [pdf]
  • Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier. ICML 2023.
• STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. [pdf]
  • Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha. ICML 2023.
• Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. [pdf]
  • Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa. ICML 2023.
• Subequivariant Graph Reinforcement Learning in 3D Environments. [pdf]
  • Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang. ICML 2023.
• Multi-task Hierarchical Adversarial Inverse Reinforcement Learning. [pdf]
  • Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal. ICML 2023.
• Semi-Offline Reinforcement Learning for Optimized Text Generation. [pdf]
  • Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan. ICML 2023.
• Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning. [pdf]
  • Dingyang Chen, Qi Zhang. ICML 2023.
• Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. [pdf]
  • Brett Daley, Martha White, Christopher Amato, Marlos C. Machado. ICML 2023.
• Reinforcement Learning Can Be More Efficient with Multiple Rewards. [pdf]
  • Christoph Dann, Yishay Mansour, Mehryar Mohri. ICML 2023.
• Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models. [pdf]
  • Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone. 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.
• 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.
• Hyperparameters in Reinforcement Learning and How To Tune Them. [pdf]
  • Theresa Eimer, Marius Lindauer, Roberta Raileanu. ICML 2023.
• A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. [pdf]
  • Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov. ICML 2023.
• Non-stationary Reinforcement Learning under General Function Approximation. [pdf]
  • Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang. ICML 2023.
• Graph Reinforcement Learning for Network Control via Bi-Level Optimization. [pdf]
  • Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira. ICML 2023.
• A Reinforcement Learning Framework for Dynamic Mediation Analysis. [pdf]
  • Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song. ICML 2023.
• Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning. [pdf]
  • Matthias Gerstgrasser, David C. Parkes. ICML 2023.
• Reinforcement Learning from Passive Data via Latent Intentions. [pdf]
  • Dibya Ghosh, Chethan Anand Bhateja, Sergey Levine. ICML 2023.
• Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. [pdf]
  • Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu. ICML 2023.
• Language Instructed Reinforcement Learning for Human-AI Coordination. [pdf]
  • Hengyuan Hu, Dorsa Sadigh. ICML 2023.
• Reinforcement Learning in Low-rank MDPs with Density Features. [pdf]
  • Audrey Huang, Jinglin Chen, Nan Jiang. ICML 2023.
• Information-Theoretic State Space Model for Multi-View Reinforcement Learning. [pdf]
  • HyeongJoo Hwang, Seokin Seo, Youngsoo Jang, Sungyoon Kim, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim. ICML 2023.
• Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. [pdf]
  • Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra. ICML 2023.
• Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum. [pdf]
  • Jigang Kim, Daesol Cho, H. Jin Kim. ICML 2023.
• LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework. [pdf]
  • Woojun Kim, Jeonghye Kim, Youngchul Sung. ICML 2023.
• Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. [pdf]
  • Seongun Kim, Kyowoon Lee, Jaesik Choi. ICML 2023.
• Model-based Offline Reinforcement Learning with Count-based Conservatism. [pdf]
  • Byeongchan Kim, Min Hwan Oh. ICML 2023.
• An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning. [pdf]
  • Woojun Kim, Youngchul Sung. ICML 2023.
• Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions. [pdf]
  • Ezgi Korkmaz, Jonah Brown-Cohen. ICML 2023.
• Variance Control for Distributional Reinforcement Learning. [pdf]
  • Qi Kuang, Zhoufan Zhu, Liwen Zhang, Fan Zhou. ICML 2023.
• Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning. [pdf]
  • Aqeel Labash, Florian Stelzer, Daniel Majoral, Raul Vicente Zafra. ICML 2023.
• Bootstrapped Representations in Reinforcement Learning. [pdf]
  • Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney. ICML 2023.
• On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning. [pdf]
  • Hojoon Lee, Koanho Lee, Dongyoon Hwang, Hyunho Lee, Byungkun Lee, Jaegul Choo. ICML 2023.
• MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations. [pdf]
  • Anqi Li, Byron Boots, Ching-An Cheng. ICML 2023.
• Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. [pdf]
  • Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal. 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.
• Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. [pdf]
  • Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang. ICML 2023.
• Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. [pdf]
  • Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang. ICML 2023.
• Internally Rewarded Reinforcement Learning. [pdf]
  • Mengdi Li, Xufeng Zhao, Jae Hee Lee, Cornelius Weber, Stefan Wermter. ICML 2023.
• Safe Offline Reinforcement Learning with Real-Time Budget Constraints. [pdf]
  • Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang. ICML 2023.
• Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data. [pdf]
  • Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Yihang Yao, Hanjiang Hu, Ding Zhao. ICML 2023.
• Constrained Decision Transformer for Offline Safe Reinforcement Learning. [pdf]
  • Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao. ICML 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.
• 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.
• 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.
• Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning. [pdf]
  • Sam Lobel, Akhil Bagaria, George Konidaris. ICML 2023.
• Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning. [pdf]
  • Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu. ICML 2023.
• Performative Reinforcement Learning. [pdf]
  • Debmalya Mandal, Stelios Triantafyllou, Goran Radanovic. ICML 2023.
• Supported Trust Region Optimization for Offline Reinforcement Learning. [pdf]
  • Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji. ICML 2023.
• Towards Theoretical Understanding of Inverse Reinforcement Learning. [pdf]
  • Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli. ICML 2023.
• Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. [pdf]
  • Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu. ICML 2023.
• ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. [pdf]
  • Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy. ICML 2023.
• Representation-Driven Reinforcement Learning. [pdf]
  • Ofir Nabati, Guy Tennenholtz, Shie Mannor. ICML 2023.
• Multi-User Reinforcement Learning with Low Rank Rewards. [pdf]
  • Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain. 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.
• Provable Reset-free Reinforcement Learning by No-Regret Reduction. [pdf]
  • Hoai-An Nguyen, Ching-An Cheng. ICML 2023.
• Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators. [pdf]
  • Paavo Parmas, Takuma Seno, Yuma Aoki. 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.
• Truncating Trajectories in Monte Carlo Reinforcement Learning. [pdf]
  • Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli. ICML 2023.
• Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. [pdf]
  • Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron C. Courville, Alexandre Lacoste. ICML 2023.
• Policy Regularization with Dataset Constraint for Offline Reinforcement Learning. [pdf]
  • Yuhang Ran, Yi-Chen Li, Fuxiang Zhang, Zongzhang Zhang, Yang Yu. ICML 2023.
• The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. [pdf]
  • Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez. ICML 2023.
• RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents. [pdf]
  • Rafael Rodríguez-Sánchez, Benjamin Adin Spiegel, Jennifer Wang, Roma Patel, Stefanie Tellex, George Konidaris. ICML 2023.
• Posterior Sampling for Deep Reinforcement Learning. [pdf]
  • Remo Sasso, Michelangelo Conserva, Paulo E. Rauber. ICML 2023.
• Identifiability and Generalizability in Constrained Inverse Reinforcement Learning. [pdf]
  • Andreas Schlaginhaufen, Maryam Kamgarpour. 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.
• TGRL: An Algorithm for Teacher Guided Reinforcement Learning. [pdf]
  • Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal. ICML 2023.
• Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation. [pdf]
  • Uri Sherman, Tomer Koren, Yishay Mansour. ICML 2023.
• A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints. [pdf]
  • Ming Shi, Yingbin Liang, Ness B. Shroff. ICML 2023.
• Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. [pdf]
  • Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang. ICML 2023.
• SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning. [pdf]
  • Dongseok Shim, Seungjae Lee, H. Jin Kim. ICML 2023.
• The Dormant Neuron Phenomenon in Deep Reinforcement Learning. [pdf]
  • Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci. ICML 2023.
• Adversarial Learning of Distributional Reinforcement Learning. [pdf]
  • Yang Sui, Yukun Huang, Hongtu Zhu, Fan Zhou. ICML 2023.
• Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning. [pdf]
  • Yihao Sun, Jiaji Zhang, Chengxing Jia, Haoxin Lin, Junyin Ye, Yang Yu. ICML 2023.
• Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic. [pdf]
  • Wesley A. Suttle, Amrit S. Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel, Dinesh Manocha. ICML 2023.
• Inverse Reinforcement Learning without Reinforcement Learning. [pdf]
  • Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu. ICML 2023.
• Understanding Self-Predictive Learning for Reinforcement Learning. [pdf]
  • Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko. ICML 2023.
• Reinforcement Learning with History Dependent Dynamic Contexts. [pdf]
  • Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier. ICML 2023.
• Jump-Start Reinforcement Learning. [pdf]
  • Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman. ICML 2023.
• Leveraging Offline Data in Online Reinforcement Learning. [pdf]
  • Andrew Wagenmaker, Aldo Pacchiano. ICML 2023.
• Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. [pdf]
  • Kaiwen Wang, Nathan Kallus, Wen Sun. ICML 2023.
• GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. [pdf]
  • Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai. ICML 2023.
• Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. [pdf]
  • Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang. ICML 2023.
• Model-Free Robust Average-Reward Reinforcement Learning. [pdf]
  • Yue Wang, Alvaro Velasquez, George K. Atia, Ashley Prater-Bennette, Shaofeng Zou. ICML 2023.
• Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments. [pdf]
  • Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu. 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.
• Reachability-Aware Laplacian Representation in Reinforcement Learning. [pdf]
  • Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang. ICML 2023.
• Set-membership Belief State-based Reinforcement Learning for POMDPs. [pdf]
  • Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang. ICML 2023.
• Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. [pdf]
  • Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang. ICML 2023.
• Boosting Offline Reinforcement Learning with Action Preference Query. [pdf]
  • Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin, Shiji Song, Gao Huang. ICML 2023.
• An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning. [pdf]
  • Jaesik Yoon, Yi-Fu Wu, Heechul Bae, Sungjin Ahn. ICML 2023.
• The Benefits of Model-Based Generalization in Reinforcement Learning. [pdf]
  • Kenny John Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber. ICML 2023.
• Actor-Critic Alignment for Offline-to-Online Reinforcement Learning. [pdf]
  • Zishun Yu, Xinhua Zhang. ICML 2023.
• Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning. [pdf]
  • Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng. ICML 2023.
• When is Realizability Sufficient for Off-Policy Reinforcement Learning? [pdf]
  • Andrea Zanette. ICML 2023.
• Interactive Object Placement with Reinforcement Learning. [pdf]
  • Shengping Zhang, Quanling Meng, Qinglin Liu, Liqiang Nie, Bineng Zhong, Xiaopeng Fan, Rongrong Ji. ICML 2023.
• Robust Situational Reinforcement Learning in Face of Context Disturbances. [pdf]
  • Jinpeng Zhang, Yufeng Zheng, Chuheng Zhang, Li Zhao, Lei Song, Yuan Zhou, Jiang Bian. ICML 2023.
• Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. [pdf]
  • Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee. ICML 2023.
• Simplified Temporal Consistency Reinforcement Learning. [pdf]
  • Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen. ICML 2023.
• Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. [pdf]
  • Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover. ICML 2023.
• Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes. [pdf]
  • Runlong Zhou, Ruosong Wang, Simon Shaolei Du. ICML 2023.
• Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments. [pdf]
  • Runlong Zhou, Zihan Zhang, Simon Shaolei Du. ICML 2023.
• Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. [pdf]
  • Banghua Zhu, Michael I. Jordan, Jiantao Jiao. 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.
• Comparison of Model-Based and Model-Free Reinforcement Learning for Real-World Dexterous Robotic Manipulation Tasks. [pdf]
  • David Valencia, John Jia, Raymond Li, Alex Hayashi, Megan Lecchi, Reuel Terezakis, Trevor Gee, Minas V. Liarokapis, Bruce A. MacDonald, Henry Williams. ICRA 2023.
• Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control. [pdf]
  • Murad Dawood, Nils Dengler, Jorge de Heuvel, Maren Bennewitz. 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.
• Variable Admittance Interaction Control of UAVs via Deep Reinforcement Learning. [pdf]
  • Yuting Feng, Chuanbeibei Shi, Jianrui Du, Yushu Yu, Fuchun Sun, Yixu Song. ICRA 2023.
• Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation. [pdf]
  • Jack Saunders, Sajad Saeedi, Wenbin Li. ICRA 2023.
• RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments. [pdf]
  • Aakriti Agrawal, Amrit Singh Bedi, Dinesh Manocha. ICRA 2023.
• Reinforcement Learning-Based Optimal Multiple Waypoint Navigation. [pdf]
  • Christos Vlachos, Panagiotis Rousseas, Charalampos P. Bechlioulis, Kostas J. Kyriakopoulos. 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.
• Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning. [pdf]
  • Kyowoon Lee, Seongun Kim, Jaesik Choi. ICRA 2023.
• Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture. [pdf]
  • Johannes Pitz, Lennart Röstel, Leon Sievers, Berthold Bäuml. ICRA 2023.
• Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. [pdf]
  • Yi Liu, Gaurav Datta, Ellen R. Novoseller, Daniel S. Brown. ICRA 2023.
• Active Predictive Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems. [pdf]
  • Alexander Ororbia, Ankur Arjun Mali. ICRA 2023.
• Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters. [pdf]
  • Wei Wang, Xiaojing Cao, Alejandro Gonzalez-Garcia, Lianhao Yin, Niklas Hagemann, Yuanyuan Qiao, Carlo Ratti, Daniela Rus. ICRA 2023.
• Event-Triggered Optimal Formation Tracking Control Using Reinforcement Learning for Large-Scale UAV Systems. [pdf]
  • Ziwei Yan, Liang Han, Xiaoduo Li, Jinjie Li, Zhang Ren. ICRA 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.
• Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers. [pdf]
  • Bryan Habas, Jack W. Langelaan, Bo Cheng. ICRA 2023.
• Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand. [pdf]
  • Daniel Garces, Sushmita Bhattacharya, Stephanie Gil, Dimitri P. Bertsekas. ICRA 2023.
• Reinforcement Learning Based Pushing and Grasping Objects from Ungraspable Poses. [pdf]
  • Hao Zhang, Hongzhuo Liang, Lin Cong, Jianzhi Lyu, Long Zeng, Pingfa Feng, Jianwei Zhang. ICRA 2023.
• Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach. [pdf]
  • Wenxing Liu, Hanlin Niu, Wei Pan, Guido Herrmann, Joaquín Carrasco. ICRA 2023.
• Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot. [pdf]
  • Tao Huang, Kai Chen, Bin Li, Yun-Hui Liu, Qi Dou. ICRA 2023.
• GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback. [pdf]
  • Jie Huang, Jiangshan Hao, Rongshun Juan, Randy Gomez, Keisuke Nakamura, Guangliang Li. ICRA 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.
• Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning. [pdf]
  • Abraham George, Alison Bartsch, Amir Barati Farimani. 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.
• DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning. [pdf]
  • I Made Aswin Nahrendra, Byeongho Yu, Hyun Myung. ICRA 2023.
• Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning. [pdf]
  • Eric Vollenweider, Marko Bjelonic, Victor Klemm, Nikita Rudin, Joonho Lee, Marco Hutter. ICRA 2023.
• Deep Reinforcement Learning based Personalized Locomotion Planning for Lower-Limb Exoskeletons. [pdf]
  • Javad Khodaei-Mehr, Eddie Guo, Mojtaba Akbari, Vivian K. Mushahwar, Mahdi Tavakoli. ICRA 2023.
• Automatic Cell Rotation Method Based on Deep Reinforcement Learning. [pdf]
  • Huiying Gong, Yujie Zhang, Yaowei Liu, Qili Zhao, Xin Zhao, Mingzhu Sun. 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.
• Reinforcement Learning with Probabilistically Safe Control Barrier Functions for Ramp Merging. [pdf]
  • Soumith Udatha, Yiwei Lyu, John M. Dolan. ICRA 2023.
• Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms. [pdf]
  • Resul Dagdanov, Halil Durmus, Nazim Kemal Ure. ICRA 2023.
• Uncertainty-Guided Active Reinforcement Learning with Bayesian Neural Networks. [pdf]
  • Xinyang Wu, Mohamed El-Shamouty, Christof Nitsche, Marco F. Huber. ICRA 2023.
• Efficient Planning of Multi-Robot Collective Transport using Graph Reinforcement Learning with Higher Order Topological Abstraction. [pdf]
  • Steve Paul, Wenyuan Li, Brian Smyth, Yuzhou Chen, Yulia R. Gel, Souma Chowdhury. ICRA 2023.
• Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control. [pdf]
  • Jiayu Chen, Tian Lan, Vaneet Aggarwal. ICRA 2023.
• Meta-Reinforcement Learning via Language Instructions. [pdf]
  • Zhenshan Bing, Alexander W. Koch, Xiangtong Yao, Kai Huang, Alois Knoll. ICRA 2023.
• Feature Extraction for Effective and Efficient Deep Reinforcement Learning on Real Robotic Platforms. [pdf]
  • Peter Böhm, Pauline Pounds, Archie C. Chapman. ICRA 2023.
• Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation. [pdf]
  • Luca Marzari, Enrico Marchesini, Alessandro Farinelli. ICRA 2023.
• Deep Reinforcement Learning for Autonomous Driving using High-Level Heterogeneous Graph Representations. [pdf]
  • Maximilian Schier, Christoph Reinders, Bodo Rosenhahn. ICRA 2023.
• Multi-Alpha Soft Actor-Critic: Overcoming Stochastic Biases in Maximum Entropy Reinforcement Learning. [pdf]
  • Conor Igoe, Swapnil Pande, Siddarth Venkatraman, Jeff G. Schneider. 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.
• Real World Offline Reinforcement Learning with Realistic Data Source. [pdf]
  • Gaoyue Zhou, Liyiming Ke, Siddhartha S. Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar. ICRA 2023.
• Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization. [pdf]
  • Thomas Lew, Sumeet Singh, Mario Prats, Jeffrey Bingham, Jonathan Weisz, Benjie Holson, Xiaohan Zhang, Vikas Sindhwani, Yao Lu, Fei Xia, Peng Xu, Tingnan Zhang, Jie Tan, Montserrat Gonzalez. ICRA 2023.
• Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning. [pdf]
  • Cheng Liu, Erik-Jan van Kampen, Guido C. H. E. de Croon. ICRA 2023.
• Aligning Human Preferences with Baseline Objectives in Reinforcement Learning. [pdf]
  • Daniel Marta, Simon Holk, Christian Pek, Jana Tumova, Iolanda Leite. ICRA 2023.
• Benchmarking Reinforcement Learning Techniques for Autonomous Navigation. [pdf]
  • Zifan Xu, Bo Liu, Xuesu Xiao, Anirudh Nair, Peter Stone. ICRA 2023.
• Conflict-constrained Multi-agent Reinforcement Learning Method for Parking Trajectory Planning. [pdf]
  • Siyuan Chen, Meiling Wang, Yi Yang, Wenjie Song. ICRA 2023.
• Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers. [pdf]
  • Yan Wang, Gautham Vasan, A. Rupam Mahmood. ICRA 2023.
• Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions. [pdf]
  • Desong Du, Shaohang Han, Naiming Qi, Haitham Bou-Ammar, Jun Wang, Wei Pan. ICRA 2023.
• Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction. [pdf]
  • Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Zhiyuan Hu, Jan Peters, Georgia Chalvatzaki. ICRA 2023.
• Sample-Efficient Goal-Conditioned Reinforcement Learning via Predictive Information Bottleneck for Goal Representation Learning. [pdf]
  • Qiming Zou, Einoshin Suzuki. ICRA 2023.
• Reinforcement Learning Control of a Reconfigurable Planar Cable Driven Parallel Manipulator. [pdf]
  • Adhiti Raman, Amey A. Salvi, Matthias J. Schmid, Venkat Krovi. ICRA 2023.
• A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces. [pdf]
  • Panagiotis Rousseas, Charalampos P. Bechlioulis, Kostas J. Kyriakopoulos. ICRA 2023.
• Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. [pdf]
  • David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley. ICRA 2023.
• Guiding Reinforcement Learning with Shared Control Templates. [pdf]
  • Abhishek Padalkar, Gabriel Quere, Franz Steinmetz, Antonin Raffin, Matthias Nieuwenhuisen, João Silvério, Freek Stulp. 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.
• Reinforcement Learning for Laser Welding Speed Control Minimizing Bead Width Error. [pdf]
  • Toshimitsu Kaneko, Gaku Minamoto, Yusuke Hirose, Tetsuo Sakai. ICRA 2023.
• Security-Aware Reinforcement Learning under Linear Temporal Logic Specifications. [pdf]
  • Bohan Cui, Keyi Zhu, Shaoyuan Li, Xiang Yin. ICRA 2023.
• Monocular Reactive Collision Avoidance for MAV Teleoperation with Deep Reinforcement Learning. [pdf]
  • Raffaele Brilli, Marco Legittimo, Francesco Crocetti, Mirko Leomanni, Mario Luca Fravolini, Gabriele Costante. ICRA 2023.

• Explainable Multi-Agent Reinforcement Learning for Temporal Queries. [pdf]
  • Kayla Boggess, Sarit Kraus, Lu Feng. IJCAI 2023.
• Controlling Neural Style Transfer with Deep Reinforcement Learning. [pdf]
  • Chengming Feng, Jing Hu, Xin Wang, Shu Hu, Bin Zhu, Xi Wu, Hongtu Zhu, Siwei Lyu. 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.
• Robust Reinforcement Learning via Progressive Task Sequence. [pdf]
  • Yike Li, Yunzhe Tian, Endong Tong, Wenjia Niu, Jiqiang Liu. IJCAI 2023.
• Adversarial Behavior Exclusion for Safe Reinforcement Learning. [pdf]
  • Md Asifur Rahman, Tongtong Liu, Sarra Alqahtani. IJCAI 2023.
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning. [pdf]
  • Yinda Chen, Wei Huang, Shenglong Zhou, Qi Chen, Zhiwei Xiong. IJCAI 2023.
• Enhancing Network by Reinforcement Learning and Neural Confined Local Search. [pdf]
  • Qifu Hu, Ruyang Li, Qi Deng, Yaqian Zhao, Rengang Li. IJCAI 2023.
• Reinforcement Learning Approaches for Traffic Signal Control under Missing Data. [pdf]
  • Hao Mei, Junxian Li, Bin Shi, Hua Wei. IJCAI 2023.
• Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning. [pdf]
  • Sen Zhao, Wei Wei, Yifan Liu, Ziyang Wang, Wendi Li, Xian-Ling Mao, Shuai Zhu, Minghui Yang, Zujie Wen. IJCAI 2023.
• A Low Latency Adaptive Coding Spike Framework for Deep Reinforcement Learning. [pdf]
  • Lang Qin, Rui Yan, Huajin Tang. IJCAI 2023.
• CROP: Towards Distributional-Shift Robust Reinforcement Learning Using Compact Reshaped Observation Processing. [pdf]
  • Philipp Altmann, Fabian Ritz, Leonard Feuchtinger, Jonas Nüßlein, Claudia Linnhoff-Popien, Thomy Phan. IJCAI 2023.
• Ensemble Reinforcement Learning in Continuous Spaces - A Hierarchical Multi-Step Approach for Policy Training. [pdf]
  • Gang Chen, Victoria Huang. IJCAI 2023.
• Automatic Truss Design with Reinforcement Learning. [pdf]
  • Weihua Du, Jinglun Zhao, Chao Yu, Xingcheng Yao, Zimeng Song, Siyang Wu, Ruifeng Luo, Zhiyuan Liu, Xianzhong Zhao, Yi Wu. IJCAI 2023.
• SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations. [pdf]
  • Chan Kim, JaeKyung Cho, Christophe Bobda, Seung-Woo Seo, Seong-Woo Kim. IJCAI 2023.
• Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees. [pdf]
  • Daqian Shao, Marta Kwiatkowska. IJCAI 2023.
• Guide to Control: Offline Hierarchical Reinforcement Learning Using Subgoal Generation for Long-Horizon and Sparse-Reward Tasks. [pdf]
  • Wonchul Shin, Yusung Kim. 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.
• On the Reuse Bias in Off-Policy Reinforcement Learning. [pdf]
  • Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu. IJCAI 2023.
• Explainable Reinforcement Learning via a Causal World Model. [pdf]
  • Zhongwei Yu, Jingqing Ruan, Dengpeng Xing. IJCAI 2023.
• Adaptive Reward Shifting Based on Behavior Proximity for Offline Reinforcement Learning. [pdf]
  • Zhe Zhang, Xiaoyang Tan. 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.
• Causal Deep Reinforcement Learning Using Observational Data. [pdf]
  • Wenxuan Zhu, Chao Yu, Qiang Zhang. IJCAI 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.
• Towards Generalizable Reinforcement Learning for Trade Execution. [pdf]
  • Chuheng Zhang, Yitong Duan, Xiaoyu Chen, Jianyu Chen, Jian Li, Li Zhao. IJCAI 2023.
• Complex Contagion Influence Maximization: A Reinforcement Learning Approach. [pdf]
  • Haipeng Chen, Bryan Wilder, Wei Qiu, Bo An, Eric Rice, Milind Tambe. IJCAI 2023.
• Safe Reinforcement Learning via Probabilistic Logic Shields. [pdf]
  • Wen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt. IJCAI 2023.
• Building a Personalized Messaging System for Health Intervention in Underprivileged Regions Using Reinforcement Learning. [pdf]
  • Sarah Eve Kinsey, Jack Wolf, Nalini Saligram, Varun Ramesan, Meeta Walavalkar, Nidhi Jaswal, Sandhya Ramalingam, Arunesh Sinha, Thanh Hong Nguyen. IJCAI 2023.
• Planning Multiple Epidemic Interventions with Reinforcement Learning. [pdf]
  • Anh L. Mai, Nikunj Gupta, Azza Abouzied, Dennis E. Shasha. IJCAI 2023.
• Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves. [pdf]
  • Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Avisek Naug, Alexandre Pichard, Mathieu Cocho. IJCAI 2023.
• Optimizing Crop Management with Reinforcement Learning and Imitation Learning. [pdf]
  • Ran Tao, Pan Zhao, Jing Wu, Nicolas F. Martin, Matthew T. Harrison, Carla Sofia Santos Ferreira, Zahra Kalantari, Naira Hovakimyan. IJCAI 2023.
• Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework. [pdf]
  • Elias Z. Tragos, Diarmuid O'Reilly-Morgan, James Geraci, Bichen Shi, Barry Smyth, Cailbhe Doherty, Aonghus Lawlor, Neil Hurley. IJCAI 2023.
• State-wise Safe Reinforcement Learning: A Survey. [pdf]
  • Weiye Zhao, Tairan He, Rui Chen, Tianhao Wei, Changliu Liu. IJCAI 2023.
• Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning (Extended Abstract). [pdf]
  • Xiaoteng Ma, Shuai Ma, Li Xia, Qianchuan Zhao. IJCAI 2023.
• Reinforcement Learning from Optimization Proxy for Ride-Hailing Vehicle Relocation (Extended Abstract). [pdf]
  • Enpeng Yuan, Wenbo Chen, Pascal Van Hentenryck. IJCAI 2023.
• SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning. [pdf]
  • Baihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf. IJCAI 2023.