This is a collection of research and review papers for offline reinforcement learning (offline rl). Feel free to star and fork.
Maintainers:
- Haruka Kiyohara (Cornell University)
- Yuta Saito (Hanjuku-kaso Co., Ltd. / Cornell University)
We are looking for more contributors and maintainers! Please feel free to pull requests.
format:
- [title](paper link) [links]
- author1, author2, and author3. arXiv/conferences/journals/, year.
For any questions, feel free to contact: hk844@cornell.edu
- Papers
- Open Source Software/Implementations
- Blog/Podcast
- Related Workshops
- Tutorials/Talks/Lectures
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
- Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Segerie, Micah Carroll, Andi Peng, Phillip Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Bıyık, Anca Dragan, David Krueger, Dorsa Sadigh, and Dylan Hadfield-Menell. arXiv, 2023.
- A Survey on Offline Model-Based Reinforcement Learning
- Haoyang He. arXiv, 2023.
- Foundation Models for Decision Making: Problems, Methods, and Opportunities
- Sherry Yang, Ofir Nachum, Yilun Du, Jason Wei, Pieter Abbeel, Dale Schuurmans. arXiv, 2023.
- A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
- Rafael Figueiredo Prudencio, Marcos R. O. A. Maximo, and Esther Luna Colombini. arXiv, 2022.
- Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
- Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. arXiv, 2020.
- A Review of Off-Policy Evaluation in Reinforcement Learning
- Masatoshi Uehara, Chengchun Shi, and Nathan Kallus. arXiv, 2022.
- On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
- Xiaocong Chen, Siyu Wang, Julian McAuley, Dietmar Jannach, and Lina Yao. arXiv, 2023.
- Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization
- Mohamed-Amine Chadi and Hajar Mousannif. arXiv, 2023.
- Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives
- Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, and Maarten de Rijke. arXiv, 2023.
- A Survey on Transformers in Reinforcement Learning
- Wenzhe Li, Hao Luo, Zichuan Lin, Chongjie Zhang, Zongqing Lu, and Deheng Ye. arXiv, 2023.
- Deep Reinforcement Learning: Opportunities and Challenges
- Yuxi Li. arXiv, 2022.
- A Survey on Model-based Reinforcement Learning
- Fan-Ming Luo, Tian Xu, Hang Lai, Xiong-Hui Chen, Weinan Zhang, and Yang Yu. arXiv, 2022.
- Survey on Fair Reinforcement Learning: Theory and Practice
- Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, and Mykola Pechenizkiy. arXiv, 2022.
- Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
- Haruka Kiyohara, Kosuke Kawakami, and Yuta Saito. arXiv, 2021.
- A Survey of Generalisation in Deep Reinforcement Learning
- Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. arXiv, 2021.
- Value-Aided Conditional Supervised Learning for Offline RL
- Jeonghye Kim, Suyoung Lee, Woojun Kim, and Youngchul Sung. arXiv, 2024.
- Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
- Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Junqiao Zhao, and Pheng-Ann Heng. arXiv, 2024.
- DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
- Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, and Weinan Zhang. arXiv, 2024.
- Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning
- Abdelhakim Benechehab, Albert Thomas, and Balázs Kégl. arXiv, 2024.
- Context-Former: Stitching via Latent Conditioned Sequence Modeling
- Ziqi Zhang, Jingzehua Xu, Zifeng Zhuang, Jinxin Liu, and Donglin wang. arXiv, 2024.
- Adversarially Trained Actor Critic for offline CMDPs
- Honghao Wei, Xiyue Peng, Xin Liu, and Arnob Ghosh. arXiv, 2024.
- Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
- Yuanzhao Zhai, Yiying Li, Zijian Gao, Xudong Gong, Kele Xu, Dawei Feng, Ding Bo, and Huaimin Wang. arXiv, 2024.
- Solving Continual Offline Reinforcement Learning with Decision Transformer
- Kaixin Huang, Li Shen, Chen Zhao, Chun Yuan, and Dacheng Tao. arXiv, 2024.
- MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning
- Mao Hong, Zhiyue Zhang, Yue Wu, and Yanxun Xu. arXiv, 2024.
- Reframing Offline Reinforcement Learning as a Regression Problem
- Prajwal Koirala and Cody Fleming. arXiv, 2024.
- Efficient Two-Phase Offline Deep Reinforcement Learning from Preference Feedback
- Yinglun Xu and Gagandeep Singh. arXiv, 2024.
- Policy-regularized Offline Multi-objective Reinforcement Learning
- Qian Lin, Chao Yu, Zongkai Liu, and Zifan Wu. arXiv, 2024.
- Differentiable Tree Search in Latent State Space
- Dixant Mittal and Wee Sun Lee. arXiv, 2024.
- Learning from Sparse Offline Datasets via Conservative Density Estimation
- Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, and Ding Zhao. ICLR, 2024.
- Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
- Yinan Zheng, Jianxiong Li, Dongjie Yu, Yujie Yang, Shengbo Eben Li, Xianyuan Zhan, and Jingjing Liu. ICLR, 2024.
- PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
- Hangyu Mao, Rui Zhao, Ziyue Li, Zhiwei Xu, Hao Chen, Yiqun Chen, Bin Zhang, Zhen Xiao, Junge Zhang, and Jiangjin Yin. AAMAS, 2024.
- Critic-Guided Decision Transformer for Offline Reinforcement Learning
- Yuanfu Wang, Chao Yang, Ying Wen, Yu Liu, and Yu Qiao. AAAI, 2024.
- CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning
- Chenyu Sun, Hangwei Qian, and Chunyan Miao. AAAI, 2024.
- Neural Network Approximation for Pessimistic Offline Reinforcement Learning
- Di Wu, Yuling Jiao, Li Shen, Haizhao Yang, and Xiliang Lu. AAAI, 2024.
- A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
- Yinmin Zhang, Jie Liu, Chuming Li, Yazhe Niu, Yaodong Yang, Yu Liu, and Wanli Ouyang. AAAI, 2024.
- The Generalization Gap in Offline Reinforcement Learning
- Ishita Mediratta, Qingfei You, Minqi Jiang, and Roberta Raileanu. arXiv, 2023.
- Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills
- Hongcai He, Anjie Zhu, Shuang Liang, Feiyu Chen, and Jie Shao. arXiv, 2023.
- MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
- Xiao-Yin Liu, Xiao-Hu Zhou, Guo-Tao Li, Hao Li, Mei-Jiang Gui, Tian-Yu Xiang, De-Xing Huang, and Zeng-Guang Hou. arXiv, 2023.
- Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
- Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, and Jan Peters. arXiv, 2023.
- Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning
- Pankayaraj Pathmanathan, Natalia Díaz-Rodríguez, and Javier Del Ser. arXiv, 2023.
- Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning
- Melrose Roderick, Gaurav Manek, Felix Berkenkamp, and J. Zico Kolter. arXiv, 2023.
- Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
- Yifei Zhou, Ayush Sekhari, Yuda Song, and Wen Sun. arXiv, 2023.
- Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning
- Qinjie Lin, Han Liu, and Biswa Sengupta. arXiv, 2023.
- Hierarchical Decision Transformer
- André Correia and Luís A. Alexandre. arXiv, 2023.
- Prompt-Tuning Decision Transformer with Preference Ranking
- Shengchao Hu, Li Shen, Ya Zhang, and Dacheng Tao. arXiv, 2023.
- Context Shift Reduction for Offline Meta-Reinforcement Learning
- Yunkai Gao, Rui Zhang, Jiaming Guo, Fan Wu, Qi Yi, Shaohui Peng, Siming Lan, Ruizhi Chen, Zidong Du, Xing Hu, Qi Guo, Ling Li, and Yunji Chen. arXiv, 2023.
- Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
- Kun Lei, Zhengmao He, Chenhao Lu, Kaizhe Hu, Yang Gao, and Huazhe Xu. arXiv, 2023.
- Score Models for Offline Goal-Conditioned Reinforcement Learning
- Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, and Scott Niekum. arXiv, 2023.
- Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
- Joey Hong, Anca Dragan, and Sergey Levine. arXiv, 2023.
- Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspective
- Xuejie Liu, Anji Liu, Guy Van den Broeck, and Yitao Liang. arXiv, 2023.
- Rethinking Decision Transformer via Hierarchical Reinforcement Learning
- Yi Ma, Chenjun Xiao, Hebin Liang, and Jianye Hao. arXiv, 2023.
- Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
- Ruizhe Shi, Yuyao Liu, Yanjie Ze, Simon S. Du, and Huazhe Xu. arXiv, 2023.
- GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
- Mianchu Wang, Rui Yang, Xi Chen, and Meng Fang. arXiv, 2023.
- SERA: Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning
- Ziqi Zhang, Xiao Xiong, Zifeng Zhuang, Jinxin Liu, and Donglin Wang. arXiv, 2023.
- Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage
- Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, and Mohammad Ghavamzadeh. arXiv, 2023.
- Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning
- Nicholas E. Corrado, Yuxiao Qu, John U. Balis, Adam Labiosa, and Josiah P. Hanna. arXiv, 2023.
- CROP: Conservative Reward for Model-based Offline Policy Optimization
- Hao Li, Xiao-Hu Zhou, Xiao-Liang Xie, Shi-Qi Liu, Zhen-Qiu Feng, Xiao-Yin Liu, Mei-Jiang Gui, Tian-Yu Xiang, De-Xing Huang, Bo-Xian Yao, and Zeng-Guang Hou. arXiv, 2023.
- Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
- Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang, Chongjie Zhang, Lei Han, and Tong Zhang. arXiv, 2023.
- Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
- Max Sobol Mark, Archit Sharma, Fahim Tajwar, Rafael Rafailov, Sergey Levine, and Chelsea Finn. arXiv, 2023.
- Boosting Continuous Control with Consistency Policy
- Yuhui Chen, Haoran Li, and Dongbin Zhao. arXiv, 2023.
- Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
- Trevor McInroe, Stefano V. Albrecht, and Amos Storkey. arXiv, 2023.
- Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
- Fan-Ming Luo, Tian Xu, Xingchen Cao, and Yang Yu. arXiv, 2023.
- DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
- Longxiang He, Linrui Zhang, Junbo Tan, and Xueqian Wang. arXiv, 2023.
- Self-Confirming Transformer for Locally Consistent Online Adaptation in Multi-Agent Reinforcement Learning
- Tao Li, Juan Guevara, Xinghong Xie, and Quanyan Zhu. arXiv, 2023.
- Learning to Reach Goals via Diffusion
- Vineet Jain and Siamak Ravanbakhsh. arXiv, 2023.
- Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making
- Jeonghye Kim, Suyoung Lee, Woojun Kim, and Youngchul Sung. arXiv, 2023.
- Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
- Zihan Ding and Chi Jin. arXiv, 2023.
- Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
- Qiwei Di, Heyang Zhao, Jiafan He, and Quanquan Gu. arXiv, 2023.
- Reasoning with Latent Diffusion in Offline Reinforcement Learning
- Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, and Glen Berseth. arXiv, 2023.
- Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance
- Qisen Yang, Shenzhi Wang, Qihang Zhang, Gao Huang, and Shiji Song. arXiv, 2023.
- Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
- Xiaoyu Wen, Xudong Yu, Rui Yang, Chenjia Bai, and Zhen Wang. arXiv, 2023.
- Robust Offline Reinforcement Learning -- Certify the Confidence Interval
- Jiarui Yao and Simon Shaolei Du. arXiv, 2023.
- Stackelberg Batch Policy Learning
- Wenzhuo Zhou and Annie Qu. arXiv, 2023.
- H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
- Haoyi Niu, Tianying Ji, Bingqi Liu, Haocheng Zhao, Xiangyu Zhu, Jianying Zheng, Pengfei Huang, Guyue Zhou, Jianming Hu, and Xianyuan Zhan. arXiv, 2023.
- Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
- Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singht, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, and Sergey Levine. arXiv, 2023.
- DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning
- Xiao-Yin Liu, Xiao-Hu Zhou, Xiao-Liang Xie, Shi-Qi Liu, Zhen-Qiu Feng, Hao Li, Mei-Jiang Gui, Tian-Yu Xiang, De-Xing Huang, and Zeng-Guang Hou. arXiv, 2023.
- Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
- Jinning Li, Xinyi Liu, Banghua Zhu, Jiantao Jiao, Masayoshi Tomizuka, Chen Tang, and Wei Zhan. arXiv, 2023.
- Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
- Cristina Pinneri, Sarah Bechtle, Markus Wulfmeier, Arunkumar Byravan, Jingwei Zhang, William F. Whitney, and Martin Riedmiller. arXiv, 2023.
- Reasoning with Latent Diffusion in Offline Reinforcement Learning
- Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, and Glen Berseth. arXiv, 2023.
- Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance
- Qisen Yang, Shenzhi Wang, Qihang Zhang, Gao Huang, and Shiji Song. arXiv, 2023.
- Multi-Objective Decision Transformers for Offline Reinforcement Learning
- Abdelghani Ghanem, Philippe Ciblat, and Mounir Ghogho. arXiv, 2023.
- AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
- Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Richard Powell, Konrad Żołna, Julian Schrittwieser, David Choi, Petko Georgiev, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, and Oriol Vinyals. arXiv, 2023.
- Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations
- Nirbhay Modhe, Qiaozi Gao, Ashwin Kalyan, Dhruv Batra, Govind Thattai, and Gaurav Sukhatme. arXiv, 2023.
- PASTA: Pretrained Action-State Transformer Agents
- Raphael Boige, Yannis Flet-Berliac, Arthur Flajolet, Guillaume Richard, and Thomas Pierrot. arXiv, 2023.
- Towards A Unified Agent with Foundation Models
- Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, and Martin Riedmiller. arXiv, 2023.
- Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning
- Zilai Zeng, Ce Zhang, Shijie Wang, and Chen Sun. arXiv, 2023.
- Offline Reinforcement Learning with Imbalanced Datasets
- Li Jiang, Sijie Chen, Jielin Qiu, Haoran Xu, Wai Kin Chan, and Zhao Ding. arXiv, 2023.
- LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning
- Outongyi Lv, Bingxin Zhou, and Yu Guang Wang. arXiv, 2023.
- Elastic Decision Transformer
- Yueh-Hua Wu, Xiaolong Wang, and Masashi Hamaya. arXiv, 2023.
- Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning
- Jinyi Liu, Yi Ma, Jianye Hao, Yujing Hu, Yan Zheng, Tangjie Lv, and Changjie Fan. arXiv, 2023.
- Is RLHF More Difficult than Standard RL?
- Yuanhao Wang, Qinghua Liu, and Chi Jin. arXiv, 2023.
- Supervised Pretraining Can Learn In-Context Reinforcement Learning
- Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, and Emma Brunskill. arXiv, 2023.
- Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching
- H.J. Terry Suh, Glen Chou, Hongkai Dai, Lujie Yang, Abhishek Gupta, and Russ Tedrake. arXiv, 2023.
- Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
- Xiao Zhang, Hai Zhang, Hongtu Zhou, Chang Huang, Di Zhang, Chen Ye, and Junqiao Zhao. arXiv, 2023.
- CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
- Jinxin Liu, Lipeng Zu, Li He, and Donglin Wang. arXiv, 2023.
- Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting
- Zhang-Wei Hong, Pulkit Agrawal, Rémi Tachet des Combes, and Romain Laroche.
- Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
- Jinxin Liu, Ziqi Zhang, Zhenyu Wei, Zifeng Zhuang, Yachen Kang, Sibo Gai, and Donglin Wang. arXiv, 2023.
- A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
- Kihyuk Hong, Yuhang Li, and Ambuj Tewari. arXiv, 2023.
- HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
- Shixi Lian, Yi Ma, Jinyi Liu, Yan Zheng, and Zhaopeng Meng. arXiv, 2023.
- Ensemble-based Offline-to-Online Reinforcement Learning: From Pessimistic Learning to Optimistic Exploration
- Kai Zhao, Yi Ma, Jinyi Liu, Yan Zheng, and Zhaopeng Meng. arXiv, 2023.
- In-Sample Policy Iteration for Offline Reinforcement Learning
- Xiaohan Hu, Yi Ma, Chenjun Xiao, Yan Zheng, and Zhaopeng Meng. arXiv, 2023.
- Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning
- Jifeng Hu, Yanchao Sun, Sili Huang, SiYuan Guo, Hechang Chen, Li Shen, Lichao Sun, Yi Chang, and Dacheng Tao. arXiv, 2023.
- Offline Prioritized Experience Replay
- Yang Yue, Bingyi Kang, Xiao Ma, Gao Huang, Shiji Song, and Shuicheng Yan. arXiv, 2023.
- Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
- Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, and Guy Tennenholtz. arXiv, 2023.
- Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
- Jianhao Wang, Jin Zhang, Haozhe Jiang, Junyu Zhang, Liwei Wang, and Chongjie Zhang. arXiv, 2023.
- Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
- Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang, Weinan Zhang, Dong Wang, Bin Zhao, and Xuelong Li. arXiv, 2023.
- Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism
- Zihao Li, Zhuoran Yang, and Mengdi Wang. arXiv, 2023.
- MADiff: Offline Multi-agent Learning with Diffusion Models
- Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, and Weinan Zhang. arXiv, 2023.
- Provable Offline Reinforcement Learning with Human Feedback
- Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, and Wen Sun. arXiv, 2023.
- Think Before You Act: Decision Transformers with Internal Working Memory
- Jikun Kang, Romain Laroche, Xindi Yuan, Adam Trischler, Xue Liu, and Jie Fu. arXiv, 2023.
- Distributionally Robust Optimization Efficiently Solves Offline Reinforcement Learning
- Yue Wang, Yuting Hu, Jinjun Xiong, and Shaofeng Zou. arXiv, 2023.
- Offline Primal-Dual Reinforcement Learning for Linear MDPs
- Germano Gabbianelli, Gergely Neu, Nneka Okolo, and Matteo Papini. arXiv, 2023.
- Federated Offline Policy Learning with Heterogeneous Observational Data
- Aldo Gael Carranza and Susan Athey. arXiv, 2023.
- Offline Reinforcement Learning with Additional Covering Distributions
- Chenjie Mao. arXiv, 2023.
- Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
- Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, and Yuxin Chen. arXiv, 2023.
- Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems
- Bin Zhang, Hangyu Mao, Lijuan Li, Zhiwei Xu, Dapeng Li, Rui Zhao, and Guoliang Fan. arXiv, 2023.
- Federated Ensemble-Directed Offline Reinforcement Learning
- Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil, and Srinivas Shakkottai. arXiv, 2023.
- IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
- Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine. arXiv, 2023.
- Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
- Alain Andres, Lukas Schäfer, Esther Villar-Rodriguez, Stefano V.Albrecht, Javier Del Ser. arXiv, 2023.
- Reinforcement Learning from Passive Data via Latent Intentions [website]
- Dibya Ghosh, Chethan Bhateja, and Sergey Levine. arXiv, 2023.
- Uncertainty-driven Trajectory Truncation for Model-based Offline Reinforcement Learning
- Junjie Zhang, Jiafei Lyu, Xiaoteng Ma, Jiangpeng Yan, Jun Yang, Le Wan, and Xiu Li. arXiv, 2023.
- RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
- Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang. arXiv, 2023.
- Batch Quantum Reinforcement Learning
- Maniraman Periyasamy, Marc Hölle, Marco Wiedmann, Daniel D. Scherer, Axel Plinge, and Christopher Mutschler. arXiv, 2023.
- Accelerating exploration and representation learning with offline pre-training
- Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, and Ankit Anand. arXiv, 2023.
- On Context Distribution Shift in Task Representation Learning for Offline Meta RL
- Chenyang Zhao, Zihao Zhou, and Bin Liu. arXiv, 2023.
- Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
- Tongzhou Wang, Antonio Torralba, Phillip Isola, and Amy Zhang. arXiv, 2023.
- Learning Excavation of Rigid Objects with Offline Reinforcement Learning
- Shiyu Jin, Zhixian Ye, and Liangjun Zhang. arXiv, 2023.
- Goal-conditioned Offline Reinforcement Learning through State Space Partitioning
- Mianchu Wang, Yue Jin, and Giovanni Montana. arXiv, 2023.
- Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies
- Daniel Lawson and Ahmed H. Qureshi. arXiv, 2023.
- Deploying Offline Reinforcement Learning with Human Feedback
- Ziniu Li, Ke Xu, Liu Liu, Lanqing Li, Deheng Ye, and Peilin Zhao. arXiv, 2023.
- Synthetic Experience Replay
- Cong Lu, Philip J. Ball, and Jack Parker-Holder. arXiv, 2023.
- ENTROPY: Environment Transformer and Offline Policy Optimization
- Pengqin Wang, Meixin Zhu, and Shaojie Shen. arXiv, 2023.
- Graph Decision Transformer
- Shengchao Hu, Li Shen, Ya Zhang, and Dacheng Tao. arXiv, 2023.
- Selective Uncertainty Propagation in Offline RL
- Sanath Kumar Krishnamurthy, Tanmay Gangwani, Sumeet Katariya, Branislav Kveton, and Anshuka Rangi. arXiv, 2023.
- Off-the-Grid MARL: a Framework for Dataset Generation with Baselines for Cooperative Offline Multi-Agent Reinforcement Learning
- Claude Formanek, Asad Jeewa, Jonathan Shock, and Arnu Pretorius. arXiv, 2023.
- Skill Decision Transformer
- Shyam Sudhakaran and Sebastian Risi. arXiv, 2023.
- Guiding Online Reinforcement Learning with Action-Free Offline Pretraining
- Deyao Zhu, Yuhui Wang, Jürgen Schmidhuber, and Mohamed Elhoseiny. arXiv, 2023.
- SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
- Qin Zhang, Linrui Zhang, Haoran Xu, Li Shen, Bowen Wang, Yongzhe Chang, Xueqian Wang, Bo Yuan, and Dacheng Tao. arXiv, 2023.
- APAC: Authorized Probability-controlled Actor-Critic For Offline Reinforcement Learning
- Jing Zhang, Chi Zhang, Wenjia Wang, and Bing-Yi Jing. arXiv, 2023.
- Designing an offline reinforcement learning objective from scratch
- Gaon An, Junhyeok Lee, Xingdong Zuo, Norio Kosaka, Kyung-Min Kim, and Hyun Oh Song. arXiv, 2023.
- Behaviour Discriminator: A Simple Data Filtering Method to Improve Offline Policy Learning
- Qiang Wang, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Francisco Roldan Sanchez, and Stephen J. Redmond. arXiv, 2023.
- Learning to View: Decision Transformers for Active Object Detection
- Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, and Arnie Sen. arXiv, 2023.
- Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning
- Taylor W. Killian, Sonali Parbhoo, and Marzyeh Ghassemi. arXiv, 2023.
- Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization
- Chengchun Shi, Zhengling Qi, Jianing Wang, and Fan Zhou. arXiv, 2023.
- Contextual Conservative Q-Learning for Offline Reinforcement Learning
- Ke Jiang, Jiayu Yao, and Xiaoyang Tan. arXiv, 2023.
- Offline Policy Optimization in RL with Variance Regularizaton
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- Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning
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- Adversarial Model for Offline Reinforcement Learning
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- Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
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- Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
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- Offline RL with Discrete Proxy Representations for Generalizability in POMDPs
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- Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
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- AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
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- Budgeting Counterfactual for Offline RL
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- Efficient Diffusion Policies for Offline Reinforcement Learning
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- Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
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- Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
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- Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
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- Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
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- Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
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- On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond
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- Conservative Offline Policy Adaptation in Multi-Agent Games
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- Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
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- Survival Instinct in Offline Reinforcement Learning
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- Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
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- Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
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- Learning to Influence Human Behavior with Offline Reinforcement Learning
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- Residual Q-Learning: Offline and Online Policy Customization without Value
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- Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
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- Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
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- Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL
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- Corruption-Robust Offline Reinforcement Learning with General Function Approximation
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- Learning to Modulate pre-trained Models in RL
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- Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
- Jianzhun Shao, Yun Qu, Chen Chen, Hongchang Zhang, and Xiangyang Ji. NeurIPS, 2023.
- One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
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- Goal-Conditioned Predictive Coding for Offline Reinforcement Learning
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- Mutual Information Regularized Offline Reinforcement Learning
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- Offline RL With Heteroskedastic Datasets and Support Constraints
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- Offline Reinforcement Learning with Differential Privacy
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- Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples
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- Reining Generalization in Offline Reinforcement Learning via Representation Distinction
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- VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning
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- SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
- Youngsoo Jang, Geon-Hyeong Kim, Jongmin Lee, Sungryull Sohn, Byoungjip Kim, Honglak Lee, and Moontae Lee. NeurIPS, 2023.
- Hierarchical Diffusion for Offline Decision Making
- Wenhao Li, Xiangfeng Wang, Bo Jin, and Hongyuan Zha. ICML, 2023.
- MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
- Anqi Li, Byron Boots, and Ching-An Cheng. ICML, 2023.
- Safe Offline Reinforcement Learning with Real-Time Budget Constraints
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- Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
- Donghao Li, Ruiquan Huang, Cong Shen, and Jing Yang. ICML, 2023.
- A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning
- Benjamin Eysenbach, Matthieu Geist, Sergey Levine, and Ruslan Salakhutdinov. ICML, 2023.
- Anti-Exploration by Random Network Distillation
- Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, and Sergey Kolesnikov. ICML, 2023.
- Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
- Tongzhou Wang, Antonio Torralba, Phillip Isola, and Amy Zhang. ICML, 2023.
- PASTA: Pessimistic Assortment Optimization
- Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X Fang, and Vahid Tarokh. ICML, 2023.
- Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
- Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu. ICML, 2023.
- Supported Trust Region Optimization for Offline Reinforcement Learning
- Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, and Xiangyang Ji. ICML, 2023.
- Principled Offline RL in the Presence of Rich Exogenous Information
- Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Rajiv Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, and John Langford. ICML, 2023.
- Efficient Online Reinforcement Learning with Offline Data
- Philip J. Ball, Laura Smith, Ilya Kostrikov, and Sergey Levine. ICML, 2023.
- Boosting Offline Reinforcement Learning with Action Preference Query
- Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin, Shiji Song, and Gao Huang. ICML, 2023.
- Model-based Offline Reinforcement Learning with Count-based Conservatism
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- Constrained Decision Transformer for Offline Safe Reinforcement Learning
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- Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
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- Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
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- What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
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- Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
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- MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
- Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan, Yan Zheng, Bin Wang, and Zhixuan Liang. ICML, 2023.
- Distance Weighted Supervised Learning for Offline Interaction Data
- Joey Hejna, Jensen Gao, and Dorsa Sadigh. ICML, 2023.
- Masked Trajectory Models for Prediction, Representation, and Control
- Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, and Aravind Rajeswaran. ICML, 2023.
- Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
- Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu. ICML, 2023.
- Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
- Wenhao Ding, Tong Che, Ding Zhao, and Marco Pavone. ICML, 2023.
- Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
- Hang Wang, Sen Lin, and Junshan Zhang. ICML, 2023.
- Future-conditioned Unsupervised Pretraining for Decision Transformer
- Zhihui Xie, Zichuan Lin, Deheng Ye, Qiang Fu, Wei Yang, and Shuai Li. ICML, 2023.
- PAC-Bayesian Offline Contextual Bandits With Guarantees
- Otmane Sakhi, Nicolas Chopin, and Pierre Alquier. ICML, 2023.
- Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
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- Jump-Start Reinforcement Learning [website]
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- Learning Temporally AbstractWorld Models without Online Experimentation
- Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, and Howie Choset. ICML, 2023.
- A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback
- Guanyu Nie, Yididiya Y Nadew, Yanhui Zhu, Vaneet Aggarwal, and Christopher John Quinn. ICML, 2023.
- Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
- Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang, and Kaiqing Zhang. ICML, 2023.
- Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
- Qinqing Zheng, Mikael Henaff, Brandon Amos, and Aditya Grover. ICML, 2023.
- Actor-Critic Alignment for Offline-to-Online Reinforcement Learning
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- Leveraging Offline Data in Online Reinforcement Learning
- Andrew Wagenmaker and Aldo Pacchiano. ICML, 2023.
- Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
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- Offline Learning in Markov Games with General Function Approximation
- Yuheng Zhang, Yu Bai, and Nan Jiang. ICML, 2023.
- Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
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- Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL
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- Confidence-Conditioned Value Functions for Offline Reinforcement Learning
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- Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
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- Extreme Q-Learning: MaxEnt RL without Entropy
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- Dichotomy of Control: Separating What You Can Control from What You Cannot
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- From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data
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- VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
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- Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
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- The In-Sample Softmax for Offline Reinforcement Learning
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- Does Zero-Shot Reinforcement Learning Exist?
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- Behavior Prior Representation learning for Offline Reinforcement Learning
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- Mind the Gap: Offline Policy Optimization for Imperfect Rewards
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- Offline Congestion Games: How Feedback Type Affects Data Coverage Requirement
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- User-Interactive Offline Reinforcement Learning
- Phillip Swazinna, Steffen Udluft, and Thomas Runkler. ICLR, 2023.
- Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data
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- Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient [code]
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- Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting
- Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, and Romain Laroche. ICLR, 2023.
- Efficient Offline Policy Optimization with a Learned Model
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- Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
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- When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
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- In-sample Actor Critic for Offline Reinforcement Learning
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- Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning
- Deyao Zhu, Li Erran Li, and Mohamed Elhoseiny. ICLR, 2023.
- Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
- Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, and Scott Sanner. ICLR, 2023.
- Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
- Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, and Jun Zhu. ICLR, 2023.
- Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
- Ming Yin, Mengdi Wang, and Yu-Xiang Wang. ICLR, 2023.
- Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
- Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, and Tong Zhang. ICLR, 2023.
- Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
- Miao Lu, Yifei Min, Zhaoran Wang, and Zhuoran Yang. ICLR, 2023.
- Hyper-Decision Transformer for Efficient Online Policy Adaptation
- Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, and Chuang Gan. ICLR, 2023.
- Efficient Planning in a Compact Latent Action Space
- Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, and Yuandong Tian. ICLR, 2023.
- Preference Transformer: Modeling Human Preferences using Transformers for RL [website]
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- Behavior Proximal Policy Optimization
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- Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards
- Thanh Nguyen-Tang and Raman Arora. ICLR, 2023.
- The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning
- Hao Hu, Yiqin Yang, Qianchuan Zhao, and Chongjie Zhang. ICLR, 2023.
- Decision Transformer under Random Frame Dropping
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- Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
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- Finetuning Offline World Models in the Real World
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- On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples
- Mustafa O. Karabag and Ufuk Topcu. AAAI, 2023.
- Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
- Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, and Jing Jiang. AAAI, 2023.
- Safe Policy Improvement for POMDPs via Finite-State Controllers
- Thiago D. Simão, Marnix Suilen, and Nils Jansen. AAAI, 2023.
- Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning
- Guoxi Zhang and Hisashi Kashima. AAAI, 2023.
- On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
- Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, and Raman Arora. AAAI, 2023.
- Contrastive Example-Based Control
- Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, and Chelsea Finn. LDC, 2023.
- Curriculum Offline Reinforcement Learning
- Yuanying Cai, Chuheng Zhang, Hanye Zhao, Li Zhao, and Jiang Bian. AAMAS. 2023.
- Offline Reinforcement Learning with On-Policy Q-Function Regularization
- Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, and Matthieu Geist. ECML, 2023.
- Model-based Offline Policy Optimization with Adversarial Network
- Junming Yang, Xingguo Chen, Shengyuan Wang, and Bolei Zhang. ECAI, 2023.
- Efficient experience replay architecture for offline reinforcement learning
- Longfei Zhang, Yanghe Feng, Rongxiao Wang, Yue Xu, Naifu Xu, Zeyi Liu, and Hang Du. RIA, 2023.
- Automatic Trade-off Adaptation in Offline RL
- Phillip Swazinna, Steffen Udluft, and Thomas Runkler. ESANN, 2023.
- Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling
- Ashish Kumar and Ilya Kuzovkin. arXiv, 2022.
- Latent Variable Representation for Reinforcement Learning
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- Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning
- Qi Tian, Kun Kuang, Furui Liu, and Baoxiang Wang. arXiv, 2022.
- State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
- Chen Chen, Hongyao Tang, Yi Ma, Chao Wang, Qianli Shen, Dong Li, and Jianye Hao. arXiv, 2022.
- Masked Autoencoding for Scalable and Generalizable Decision Making
- Fangchen Liu, Hao Liu, Aditya Grover, and Pieter Abbeel. arXiv, 2022.
- Improving TD3-BC: Relaxed Policy Constraint for Offline Learning and Stable Online Fine-Tuning
- Alex Beeson and Giovanni Montana. arXiv, 2022.
- Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
- Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Dmitry Akimov, and Sergey Kolesnikov. arXiv, 2022.
- Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
- Dmitriy Akimov, Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, and Sergey Kolesnikov. arXiv, 2022.
- Model-based Trajectory Stitching for Improved Offline Reinforcement Learning
- Charles A. Hepburn and Giovanni Montana. arXiv, 2022.
- Offline Reinforcement Learning with Adaptive Behavior Regularization
- Yunfan Zhou, Xijun Li, and Qingyu Qu. arXiv, 2022.
- Contextual Transformer for Offline Meta Reinforcement Learning
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- Wall Street Tree Search: Risk-Aware Planning for Offline Reinforcement Learning
- Dan Elbaz, Gal Novik, and Oren Salzman. arXiv, 2022.
- ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data
- Tengyang Xie, Mohak Bhardwaj, Nan Jiang, and Ching-An Cheng. arXiv, 2022.
- Contrastive Value Learning: Implicit Models for Simple Offline RL
- Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, and Jonathan Tompson. arXiv, 2022.
- Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping
- Hao Sun, Lei Han, Rui Yang, Xiaoteng Ma, Jian Guo, and Bolei Zhou. arXiv, 2022.
- Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
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- Learning Contraction Policies from Offline Data
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- Advancing RAN Slicing with Offline Reinforcement Learning
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- Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach
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- Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning
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- A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
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- Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
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- STEER: Unified Style Transfer with Expert Reinforcement
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- Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations
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- Offline Reinforcement Learning for Optimizing Production Bidding Policies
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- End-to-end Offline Reinforcement Learning for Glycemia Control
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- Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments
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- Robotic Offline RL from Internet Videos via Value-Function Pre-Training
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- VAPOR: Holonomic Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
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- RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
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- Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
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- Reinforced Self-Training (ReST) for Language Modeling
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- Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure
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- Offline Experience Replay for Continual Offline Reinforcement Learning
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- Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
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- Data Might be Enough: Bridge Real-World Traffic Signal Control Using Offline Reinforcement Learning
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- User Retention-oriented Recommendation with Decision Transformer
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- Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
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- INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search
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- Learning Vision-based Robotic Manipulation Tasks Sequentially in Offline Reinforcement Learning Settings
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- Winning Solution of Real Robot Challenge III
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- Learning-based MPC from Big Data Using Reinforcement Learning
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- Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
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- Beyond Reward: Offline Preference-guided Policy Optimization
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- DevFormer: A Symmetric Transformer for Context-Aware Device Placement
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- On the Effectiveness of Offline RL for Dialogue Response Generation
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- Bidirectional Learning for Offline Model-based Biological Sequence Design
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- ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
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- Semi-Offline Reinforcement Learning for Optimized Text Generation
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- Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
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- Offline RL for Natural Language Generation with Implicit Language Q Learning
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- Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning
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- Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning
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- Dialog Action-Aware Transformer for Dialog Policy Learning
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- Can Offline Reinforcement Learning Help Natural Language Understanding?
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- NeurIPS 2022 Competition: Driving SMARTS
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- Controlling Commercial Cooling Systems Using Reinforcement Learning
- Jerry Luo, Cosmin Paduraru, Octavian Voicu, Yuri Chervonyi, Scott Munns, Jerry Li, Crystal Qian, Praneet Dutta, Jared Quincy Davis, Ningjia Wu, Xingwei Yang, Chu-Ming Chang, Ted Li, Rob Rose, Mingyan Fan, Hootan Nakhost, Tinglin Liu, Brian Kirkman, Frank Altamura, Lee Cline, Patrick Tonker, Joel Gouker, Dave Uden, Warren Buddy Bryan, Jason Law, Deeni Fatiha, Neil Satra, Juliet Rothenberg, Molly Carlin, Satish Tallapaka, Sims Witherspoon, David Parish, Peter Dolan, Chenyu Zhao, and Daniel J. Mankowitz.
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- Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
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- Learning-to-defer for sequential medical decision-making under uncertainty
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- Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
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- Dialogue Evaluation with Offline Reinforcement Learning
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- Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems
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- A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
- Zaharah A. Bukhsh, Nils Jansen, and Hajo Molegraaf. arXiv, 2022.
- BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion
- Fanglin Chen, Xiao Liu, Bo Tang, Feiyu Xiong, Serim Hwang, and Guomian Zhuang. arXiv, 2022.
- Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
- Zeyu Wang, Huiying Zhao, Peng Ren, Yuxi Zhou, and Ming Sheng. arXiv, 2022.
- Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
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- ARLO: A Framework for Automated Reinforcement Learning
- Marco Mussi, Davide Lombarda, Alberto Maria Metelli, Francesco Trovò, and Marcello Restelli. arXiv, 2022.
- A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment
- Yuanqi Gao and Nanpeng Yu. arXiv, 2022.
- CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning
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- Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes [code]
- Harry Emerson, Matt Guy, and Ryan McConville. arXiv, 2022.
- CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System [code]
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- A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies
- Pramod Kaushik, Sneha Kummetha, Perusha Moodley, and Raju S. Bapi. arXiv, 2022.
- Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning
- Branka Mirchevska, Moritz Werling, and Joschka Boedecker. arXiv, 2022.
- Offline Deep Reinforcement Learning for Dynamic # of Consumer Credit
- Raad Khraishi and Ramin Okhrati. arXiv, 2022.
- Offline Reinforcement Learning for Mobile Notifications
- Yiping Yuan, Ajith Muralidharan, Preetam Nandy, Miao Cheng, and Prakruthi Prabhakar. arXiv, 2022.
- Offline Reinforcement Learning for Road Traffic Control
- Mayuresh Kunjir and Sanjay Chawla. arXiv, 2022.
- Sustainable Online Reinforcement Learning for Auto-bidding
- Zhiyu Mou, Yusen Huo, Rongquan Bai, Mingzhou Xie, Chuan Yu, Jian Xu, and Bo Zheng. NeurIPS, 2022.
- Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
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- Multi-objective Optimization of Notifications Using Offline Reinforcement Learning
- Prakruthi Prabhakar, Yiping Yuan, Guangyu Yang, Wensheng Sun, and Ajith Muralidharan. KDD, 2022.
- Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
- Boxiang Lyu, Zhaoran Wang, Mladen Kolar, and Zhuoran Yang. ICML, 2022.
- GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems
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- Offline Reinforcement Learning for Visual Navigation
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- Semi-Markov Offline Reinforcement Learning for Healthcare
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- Multiple-policy High-confidence Policy Evaluation
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- Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments
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- Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation
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- Robust Offline Policy Evaluation and Optimization with Heavy-Tailed Rewards
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- Evaluation of Active Feature Acquisition Methods for Static Feature Settings
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- Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
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- Off-policy Evaluation in Doubly Inhomogeneous Environments
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- Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
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- π2vec : Policy Representations with Successor Features
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- Conformal Off-Policy Evaluation in Markov Decision Processes
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- Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
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- Oracle Inequalities for Model Selection in Offline Reinforcement Learning
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- Off-Policy Evaluation for Action-Dependent Non-stationary Environments
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- Stateful Offline Contextual Policy Evaluation and Learning
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- Off-Policy Risk Assessment for Markov Decision Processes
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- Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
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- Offline Policy Evaluation and Optimization under Confounding
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- Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
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- Safe Evaluation For Offline Learning: Are We Ready To Deploy?
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- Low Variance Off-policy Evaluation with State-based Importance Sampling
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- Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach
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- Offline Estimation of Controlled Markov Chains: Minimax Nonparametric Estimators and Sample Efficiency
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- Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
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- A Sharp Characterization of Linear Estimators for Offline Policy Evaluation
- Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, and Sham Kakade. arXiv, 2022.
- A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets [code]
- Chengchun Shi, Runzhe Wan, Ge Song, Shikai Luo, Rui Song, and Hongtu Zhu. arXiv, 2022.
- A Theoretical Framework of Almost Hyperparameter-free Hyperparameter Selection Methods for Offline Policy Evaluation
- Kohei Miyaguchi. arXiv, 2022.
- SOPE: Spectrum of Off-Policy Estimators
- Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, and Scott Niekum. NeurIPS, 2021.
- Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
- Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, and Michal Valko. NeurIPS, 2021.
- Variance-Aware Off-Policy Evaluation with Linear Function Approximation
- Yifei Min, Tianhao Wang, Dongruo Zhou, and Quanquan Gu. NeurIPS, 2021.
- Universal Off-Policy Evaluation
- Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, and Philip S. Thomas. NeurIPS, 2021.
- Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
- Siyuan Zhang and Nan Jiang. NeurIPS, 2021.
- Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings
- Ming Yin and Yu-Xiang Wang. NeurIPS, 2021.
- State Relevance for Off-Policy Evaluation
- Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, and Finale Doshi-Velez. ICML, 2021.
- Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
- Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvari, and Mengdi Wang. ICML, 2021.
- Deeply-Debiased Off-Policy Interval Estimation
- Chengchun Shi, Runzhe Wan, Victor Chernozhukov, and Rui Song. ICML, 2021.
- Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
- Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi. ICLR, 2021.
- Minimax Model Learning
- Cameron Voloshin, Nan Jiang, and Yisong Yue. AISTATS, 2021.
- Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
- Andrew Bennett, Nathan Kallus, Lihong Li, and Ali Mousavi. AISTATS, 2021.
- High-Confidence Off-Policy (or Counterfactual) Variance Estimation
- Yash Chandak, Shiv Shankar, and Philip S. Thomas. AAAI, 2021.
- Debiased Off-Policy Evaluation for Recommendation Systems
- Yusuke Narita, Shota Yasui, and Kohei Yata. RecSys, 2021.
- Pessimistic Model Selection for Offline Deep Reinforcement Learning
- Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, and Pin-Yu Chen. arXiv, 2021.
- Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes
- Andrew Bennett and Nathan Kallus. arXiv, 2021.
- Off-Policy Evaluation in Partially Observed Markov Decision Processes
- Yuchen Hu and Stefan Wager. arXiv, 2021.
- A Spectral Approach to Off-Policy Evaluation for POMDPs
- Yash Nair and Nan Jiang. arXiv, 2021.
- Projected State-action Balancing Weights for Offline Reinforcement Learnings
- Jiayi Wang, Zhengling Qi, and Raymond K.W. Wong. arXiv, 2021.
- Active Offline Policy Selection
- Ksenia Konyushkova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, and Nando de Freitas. arXiv, 2021.
- On Instrumental Variable Regression for Deep Offline Policy Evaluation
- Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, and Arnaud Doucet. arXiv, 2021.
- Average-Reward Off-Policy Policy Evaluation with Function Approximation
- Shangtong Zhang, Yi Wan, Richard S. Sutton, and Shimon Whiteson. arXiv, 2021.
- Sequential causal inference in a single world of connected units
- Aurelien Bibaut, Maya Petersen, Nikos Vlassis, Maria Dimakopoulou, and Mark van der Laan, arXiv, 2021.
- Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
- Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, and Emma Brunskill. NeurIPS, 2020.
- CoinDICE: Off-Policy Confidence Interval Estimation
- Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvari, and Dale Schuurmans. NeurIPS, 2020.
- Off-Policy Interval Estimation with Lipschitz Value Iteration
- Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, and Qiang Liu. NeurIPS, 2020.
- Off-Policy Evaluation via the Regularized Lagrangian
- Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, and Dale Schuurmans. NeurIPS, 2020.
- Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
- Nan Jiang and Jiawei Huang. NeurIPS, 2020.
- GenDICE: Generalized Offline Estimation of Stationary Values
- Ruiyi Zhang, Bo Dai, Lihong Li, and Dale Schuurmans. ICLR, 2020.
- Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies
- Xinyun Chen, Lu Wang, Yizhe Hang, Heng Ge, and Hongyuan Zha. ICLR, 2020.
- Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
- Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, and Qiang Liu. ICLR, 2020.
- Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
- Ali Mousavi, Lihong Li, Qiang Liu, and Denny Zhou. ICLR, 2020.
- GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
- Shangtong Zhang, Bo Liu, and Shimon Whiteson. ICML, 2020.
- Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
- Yaqi Duan, Zeyu Jia, and Mengdi Wang. ICML, 2020.
- Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
- Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi, Emma Brunskill, and Finale Doshi-Velez. ICML, 2020.
- Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
- Nathan Kallus and Masatoshi Uehara. ICML, 2020.
- Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
- Yao Liu, Pierre-Luc Bacon, and Emma Brunskill. ICML, 2020.
- Minimax Weight and Q-Function Learning for Off-Policy Evaluation
- Masatoshi Uehara, Jiawei Huang, and Nan Jiang. ICML, 2020.
- Accountable Off-Policy Evaluation With Kernel Bellman Statistics
- Yihao Feng, Tongzheng Ren, Ziyang Tang, and Qiang Liu. ICML, 2020.
- Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning
- Ming Yin and Yu-Xiang Wang. ICML, 2020.
- Batch Stationary Distribution Estimation
- Junfeng Wen, Bo Dai, Lihong Li, and Dale Schuurmans. ICML, 2020.
- Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control [video]
- Bingqing Chen, Ming Jin, Zhe Wang, Tianzhen Hong, and Mario Bergés, RLEM, 2020.
- Defining Admissible Rewards for High Confidence Policy Evaluation in Batch Reinforcement Learning
- Niranjani Prasad, Barbara E Engelhardt, and Finale Doshi-Velez. CHIL, 2020.
- Offline Policy Selection under Uncertainty
- Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, and Dale Schuurmans. arXiv, 2020.
- Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
- Ming Yin, Yu Bai, and Yu-Xiang Wang. arXiv, 2020.
- Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies
- Jinlin Lai, Lixin Zou, and Jiaxing Song. arXiv, 2020.
- Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning
- Rahul Singh, Liyuan Xu, and Arthur Gretton. arXiv, 2020.
- Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation
- Ilya Kostrikov and Ofir Nachum. arXiv, 2020.
- Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning
- Nathan Kallus and Masatoshi Uehara. arXiv, 2019.
- Off-Policy Evaluation in Partially Observable Environments
- Guy Tennenholtz, Uri Shalit, and Shie Mannor. AAAI, 2019.
- Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
- Nathan Kallus and Masatoshi Uehara. NeurIPS, 2019.
- Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
- Tengyang Xie, Yifei Ma, and Yu-Xiang Wang. NeuIPS, 2019.
- Off-Policy Evaluation via Off-Policy Classification
- Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, and Sergey Levine. NeuIPS, 2019.
- DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections [software]
- Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li. NeurIPS, 2019.
- Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
- Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, and Jian Peng. ICLR, 2019.
- Batch Policy Learning under Constraints [code] [website]
- Hoang M. Le, Cameron Voloshin, and Yisong Yue. ICML, 2019.
- More Efficient Off-Policy Evaluation through Regularized Targeted Learning
- Aurelien Bibaut, Ivana Malenica, Nikos Vlassis, and Mark Van Der Laan. ICML, 2019.
- Combining parametric and nonparametric models for off-policy evaluation
- Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, and Finale Doshi-Velez. ICML, 2019.
- Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
- Michael Oberst and David Sontag. ICML, 2019.
- Importance Sampling Policy Evaluation with an Estimated Behavior Policy
- Josiah Hanna, Scott Niekum, and Peter Stone. ICML, 2019.
- Representation Balancing MDPs for Off-policy Policy Evaluation
- Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, and Emma Brunskill. NeuIPS, 2018.
- Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
- Qiang Liu, Lihong Li, Ziyang Tang, and Dengyong Zhou. NeuIPS, 2018.
- More Robust Doubly Robust Off-policy Evaluation
- Mehrdad Farajtabar, Yinlam Chow, and Mohammad Ghavamzadeh. ICML, 2018.
- Importance Sampling for Fair Policy Selection
- Shayan Doroudi, Philip Thomas, and Emma Brunskill. UAI, 2017.
- Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing
- Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, and Emma Brunskill. AAAI, 2017.
- Consistent On-Line Off-Policy Evaluation
- Assaf Hallak and Shie Mannor. ICML, 2017.
- Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation
- Josiah P. Hanna, Peter Stone, and Scott Niekum. AAAMS, 2016.
- Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
- Nan Jiang and Lihong Li. ICML, 2016.
- Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
- Philip Thomas and Emma Brunskill. ICML, 2016.
- High Confidence Policy Improvement
- Philip Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. ICML, 2015.
- High Confidence Off-Policy Evaluation
- Philip S. Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. AAAI, 2015.
- Eligibility Traces for Off-Policy Policy Evaluation
- Doina Precup, Richard S. Sutton, and Satinder P. Singh. ICML, 2000.
- Sequential Counterfactual Risk Minimization
- Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, and Pierre Gaillard. ICML, 2023.
- Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
- Brett Daley, Martha White, Christopher Amato, and Marlos C. Machado. ICML, 2023.
- Multi-Task Off-Policy Learning from Bandit Feedback
- Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, and Mohammad Ghavamzadeh. ICML, 2023.
- Exponential Smoothing for Off-Policy Learning
- Imad Aouali, Victor-Emmanuel Brunel, David Rohde, and Anna Korba. ICML, 2023.
- Counterfactual Learning with General Data-generating Policies
- Yusuke Narita, Kyohei Okumura, Akihiro Shimizu, and Kohei Yata. AAAI, 2023.
- Distributionally Robust Policy Gradient for Offline Contextual Bandits
- Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, and Animashree Anandkumar. AISTATS, 2023.
- Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits
- Lequn Wang, Akshay Krishnamurthy, and Aleksandrs Slivkins. arXiv, 2023.
- Pessimistic Off-Policy Multi-Objective Optimization
- Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, and Ge Liu. arXiv, 2023.
- Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
- Zeyu Zhang, Yi Su, Hui Yuan, Yiran Wu, Rishab Balasubramanian, Qingyun Wu, Huazheng Wang, and Mengdi Wang. arXiv, 2023.
- Uncertainty-Aware Off-Policy Learning
- Xiaoying Zhang, Junpu Chen, Hongning Wang, Hong Xie, and Hang Li. arXiv, 2023.
- Fair Off-Policy Learning from Observational Data
- Dennis Frauen, Valentyn Melnychuk, and Stefan Feuerriegel. arXiv, 2023.
- Interpretable Off-Policy Learning via Hyperbox Search
- Daniel Tschernutter, Tobias Hatt, and Stefan Feuerriegel. ICML, 2022.
- Offline Policy Optimization with Eligible Actions
- Yao Liu, Yannis Flet-Berliac, and Emma Brunskill. UAI, 2022.
- Towards Robust Off-policy Learning for Runtime Uncertainty
- Da Xu, Yuting Ye, Chuanwei Ruan, and Bo Yang. AAAI, 2022.
- Safe Optimal Design with Applications in Off-Policy Learning
- Ruihao Zhu and Branislav Kveton. AISTATS, 2022.
- Off-Policy Actor-critic for Recommender Systems
- Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, and Ed Chi. RecSys, 2022.
- MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations
- Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen, and Guandong Xu. SIGIR, 2022.
- Distributionally Robust Policy Learning with Wasserstein Distance
- Daido Kido. arXiv, 2022.
- Local Policy Improvement for Recommender Systems
- Dawen Liang and Nikos Vlassis. arXiv, 2022.
- Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
- Ying Jin, Zhimei Ren, Zhuoran Yang, and Zhaoran Wang. arXiv, 2022.
- Fast Offline Policy Optimization for Large Scale Recommendation
- Otmane Sakhi, David Rohde, and Alexandre Gilotte. arXiv, 2022.
- Practical Counterfactual Policy Learning for Top-K Recommendations
- Yaxu Liu, Jui-Nan Yen, Bowen Yuan, Rundong Shi, Peng Yan, and Chih-Jen Lin. KDD, 2022.
- Boosted Off-Policy Learning
- Ben London, Levi Lu, Ted Sandler, and Thorsten Joachims. arXiv, 2022.
- Semi-Counterfactual Risk Minimization Via Neural Networks
- Gholamali Aminian, Roberto Vega, Omar Rivasplata, Laura Toni, and Miguel Rodrigues. arXiv, 2022.
- IMO^3: Interactive Multi-Objective Off-Policy Optimization
- Nan Wang, Hongning Wang, Maryam Karimzadehgan, Branislav Kveton, and Craig Boutilier. arXiv, 2022.
- Pessimistic Off-Policy Optimization for Learning to Rank
- Matej Cief, Branislav Kveton, and Michal Kompan. arXiv, 2022.
- Non-Stationary Off-Policy Optimization
- Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, and Amr Ahmed. AISTATS, 2021.
- Learning from eXtreme Bandit Feedback
- Romain Lopez, Inderjit Dhillon, and Michael I. Jordan. AAAI, 2021.
- Generalizing Off-Policy Learning under Sample Selection Bias
- Tobias Hatt, Daniel Tschernutter, and Stefan Feuerriegel. arXiv, 2021.
- Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values
- Mahed Abroshan, Kai Hou Yip, Cem Tekin, and Mihaela van der Schaar. arXiv, 2021.
- Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
- Nathan Kallus and Masatoshi Uehara. NeurIPS, 2020.
- From Importance Sampling to Doubly Robust Policy Gradient
- Jiawei Huang and Nan Jiang. ICML, 2020.
- Efficient Policy Learning from Surrogate-Loss Classification Reductions [code]
- Andrew Bennett and Nathan Kallus. ICML, 2020.
- Off-policy Bandits with Deficient Support
- Noveen Sachdeva, Yi Su, and Thorsten Joachims. KDD, 2020.
- Off-policy Learning in Two-stage Recommender Systems
- Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, and Ed H Chi. WWW, 2020.
- More Efficient Policy Learning via Optimal Retargeting
- Nathan Kallus. JASA, 2020.
- Learning When-to-Treat Policies
- Xinkun Nie, Emma Brunskill, and Stefan Wager. JASA, 2020.
- Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
- Minshuo Chen, Hao Liu, Wenjing Liao, and Tuo Zhao. arXiv, 2020.
- Bandit Overfitting in Offline Policy Learning
- David Brandfonbrener, William F. Whitney, Rajesh Ranganath, and Joan Bruna. arXiv, 2020.
- Counterfactual Learning of Continuous Stochastic Policies
- Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, and Julien Mairal. arXiv, 2020.
- Top-K Off-Policy Correction for a REINFORCE Recommender System
- Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed Chi. WSDM, 2019.
- Semi-Parametric Efficient Policy Learning with Continuous Actions
- Victor Chernozhukov, Mert Demirer, Greg Lewis, and Vasilis Syrgkanis. NeurIPS, 2019.
- Efficient Counterfactual Learning from Bandit Feedback
- Yusuke Narita, Shota Yasui, and Kohei Yata. AAAI, 2019.
- Deep Learning with Logged Bandit Feedback
- Thorsten Joachims, Adith Swaminathan, and Maarten de Rijke. ICLR, 2018.
- The Self-Normalized Estimator for Counterfactual Learning
- Adith Swaminathan and Thorsten Joachims. NeurIPS, 2015.
- Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
- Adith Swaminathan and Thorsten Joachims. ICML, 2015.
- Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
- Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, and Yuta Saito. ICLR, 2024.
- SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
- Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, and Yuta Saito. arXiv, 2023.
- Offline Policy Comparison with Confidence: Benchmarks and Baselines
- Anurag Koul, Mariano Phielipp, and Alan Fern. arXiv, 2022.
- Extending Open Bandit Pipeline to Simulate Industry Challenges
- Bram van den Akker, Niklas Weber, Felipe Moraes, and Dmitri Goldenberg. arXiv, 2022.
- Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation [software] [public dataset]
- Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita. NeurIPS, 2021.
- Evaluating the Robustness of Off-Policy Evaluation [software]
- Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno. RecSys, 2021.
- Benchmarks for Deep Off-Policy Evaluation [code]
- Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, and Thomas Paine. ICLR, 2021.
- Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning [code]
- Cameron Voloshin, Hoang M. Le, Nan Jiang, and Yisong Yue, arXiv, 2019.
- HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare
- Ge Gao, Song Ju, Markel Sanz Ausin, and Min Chi. AAMAS, 2023.
- When is Off-Policy Evaluation Useful? A Data-Centric Perspective
- Hao Sun, Alex J. Chan, Nabeel Seedat, Alihan Hüyük, and Mihaela van der Schaar. arXiv, 2023.
- Counterfactual Evaluation of Peer-Review Assignment Policies
- Martin Saveski, Steven Jecmen, Nihar B. Shah, and Johan Ugander. arXiv, 2023.
- Balanced Off-Policy Evaluation for Personalized #
- Adam N. Elmachtoub, Vishal Gupta, and Yunfan Zhao. arXiv, 2023.
- Multi-Action Dialog Policy Learning from Logged User Feedback
- Shuo Zhang, Junzhou Zhao, Pinghui Wang, Tianxiang Wang, Zi Liang, Jing Tao, Yi Huang, and Junlan Feng. arXiv, 2023.
- CFR-p: Counterfactual Regret Minimization with Hierarchical Policy Abstraction, and its Application to Two-player Mahjong
- Shiheng Wang. arXiv, 2023.
- Reward Shaping for User Satisfaction in a REINFORCE Recommender
- Konstantina Christakopoulou, Can Xu, Sai Zhang, Sriraj Badam, Trevor Potter, Daniel Li, Hao Wan, Xinyang Yi, Ya Le, Chris Berg, Eric Bencomo Dixon, Ed H. Chi, and Minmin Chen. arXiv, 2022.
- Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service
- Yuta Saito, Takuma Udagawa, and Kei Tateno. arXiv, 2021.
- Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
- Haoming Jiang, Bo Dai, Mengjiao Yang, Wei Wei, and Tuo Zhao. arXiv, 2021.
- Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings
- Shengpu Tang and Jenna Wiens. MLHC, 2021.
- Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling
- Randell Cotta, Dan Jiang, Mingyang Hu, and Peizhou Liao. WSDM, 2019.
- Offline Evaluation to Make Decisions About Playlist Recommendation
- Alois Gruson, Praveen Chandar, Christophe Charbuillet, James McInerney, Samantha Hansen, Damien Tardieu, and Ben Carterette. WSDM, 2019.
- Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
- Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, and Emma Brunskill. arXiv, 2018.
- Evaluating Reinforcement Learning Algorithms in Observational Health Settings
- Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez. arXiv, 2018.
- Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems
- Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. CIKM, 2018.
- Offline A/B testing for Recommender Systems
- Alexandre Gilotte, Clément Calauzènes, Thomas Nedelec, Alexandre Abraham, and Simon Dollé. WSDM, 2018.
- Offline Comparative Evaluation with Incremental, Minimally-Invasive Online Feedback
- Ben Carterette and Praveen Chandar. SIGIR, 2018.
- Handling Confounding for Realistic Off-Policy Evaluation
- Saurabh Sohoney, Nikita Prabhu, and Vineet Chaoji. WWW, 2018.
- Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
- Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson. JMLR, 2013.
- SCOPE-RL: A Python library for offline reinforcement learning, off-policy evaluation, and selection [paper1] [paper2] [documentation]
- Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, and Yuta Saito.
- Open Bandit Pipeline: a research framework for bandit algorithms and off-policy evaluation [paper] [documentation] [dataset]
- Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita.
- pyIEOE: Towards An Interpretable Evaluation for Offline Evaluation [paper]
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- d3rlpy: An Offline Deep Reinforcement Learning Library [paper] [website] [documentation]
- Takuma Seno and Michita Imai.
- MINERVA: An out-of-the-box GUI tool for data-driven deep reinforcement learning [website] [documentation]
- Takuma Seno and Michita Imai.
- Minari
- Farama Foundation.
- CORL: Clean Offline Reinforcement Learning [paper]
- Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, and Sergey Kolesnikov.
- COBS: Caltech OPE Benchmarking Suite [paper]
- Cameron Voloshin, Hoang M. Le, Nan Jiang, and Yisong Yue.
- Benchmarks for Deep Off-Policy Evaluation [paper]
- Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, and Thomas Paine.
- DICE: The DIstribution Correction Estimation Library [paper]
- Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li, Ruiyi Zhang, Dale Schuurmans.
- RL Unplugged: Benchmarks for Offline Reinforcement Learning [paper] [dataset]
- Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, and Nando de Freitas.
- D4RL: Datasets for Deep Data-Driven Reinforcement Learning [paper] [website]
- Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine.
- V-D4RL: Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations [paper}
- Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, and Yee Whye Teh.
- Benchmarking Offline Reinforcement Learning on Real-Robot Hardware [paper]
- Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Bernhard Schölkopf, and Georg Martius. ICLR, 2023.
- RLDS: Reinforcement Learning Datasets [paper]
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- OEF: Offline Equilibrium Finding [paper]
- Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Hau Chan, and Bo An.
- ExORL: Exploratory Data for Offline Reinforcement Learning [paper]
- Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, and Lerrel Pinto.
- RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System [paper] dataset]
- Kai Wang, Zhene Zou, Yue Shang, Qilin Deng, Minghao Zhao, Yile Liang, Runze Wu, Jianrong Tao, Xudong Shen, Tangjie Lyu, and Changjie Fan.
- NeoRL: Near Real-World Benchmarks for Offline Reinforcement Learning [paper] [website]
- Rongjun Qin, Songyi Gao, Xingyuan Zhang, Zhen Xu, Shengkai Huang, Zewen Li, Weinan Zhang, and Yang Yu.
- The Industrial Benchmark Offline RL Datasets [paper]
- Phillip Swazinna, Steffen Udluft, and Thomas Runkler.
- ARLO: A Framework for Automated Reinforcement Learning [paper]
- Marco Mussi, Davide Lombarda, Alberto Maria Metelli, Francesco Trovò, and Marcello Restelli.
- RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising [paper]
- David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou.
- MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces [paper] [documantation]
- Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Anderson Soares, Renan M. Oliveira, and Sandor Caetano.
- A Reinforcement Learning-based Volt-VAR Control Dataset [paper]
- Yuanqi Gao and Nanpeng Yu.
- Counterfactual Evaluation for Recommendation Systems
- Eugene Yan. 2022.
- Offline Reinforcement Learning: How Conservative Algorithms Can Enable New Applications
- Aviral Kumar and Avi Singh. BAIR Blog, 2020.
- AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
- Ashvin Nair and Abhishek Gupta. BAIR Blog, 2020.
- D4RL: Building Better Benchmarks for Offline Reinforcement Learning
- Justin Fu. BAIR Blog, 2020.
- Does On-Policy Data Collection Fix Errors in Off-Policy Reinforcement Learning?
- Aviral Kumar and Abhishek Gupta. BAIR Blog, 2020.
- Tackling Open Challenges in Offline Reinforcement Learning
- George Tucker and Sergey Levine. Google AI Blog, 2020.
- An Optimistic Perspective on Offline Reinforcement Learning
- Rishabh Agarwal and Mohammad Norouzi. Google AI Blog, 2020.
- Decisions from Data: How Offline Reinforcement Learning Will Change How We Use Machine Learning
- Sergey Levine. Medium, 2020.
- Introducing completely free datasets for data-driven deep reinforcement learning
- Takuma Seno. towards data science, 2020.
- Offline (Batch) Reinforcement Learning: A Review of Literature and Applications
- Daniel Seita. danieltakeshi.github.io, 2020.
- Data-Driven Deep Reinforcement Learning
- Aviral Kumar. BAIR Blog, 2019.
- AI Trends 2023: Reinforcement Learning – RLHF, Robotic Pre-Training, and Offline RL with Sergey Levine
- Sergey Levine. TWIML, 2023.
- Bandits and Simulators for Recommenders with Olivier Jeunen
- Olivier Jeunen. Recsperts, 2022.
- Sergey Levine on Robot Learning & Offline RL
- Sergey Levine. The Gradient, 2021.
- Off-Line, Off-Policy RL for Real-World Decision Making at Facebook
- Jason Gauci. TWIML, 2021.
- Xianyuan Zhan | TalkRL: The Reinforcement Learning Podcast
- Xianyuan Zhan. TWIML, 2021.
- MOReL: Model-Based Offline Reinforcement Learning with Aravind Rajeswaran
- Aravind Rajeswaran. TWIML, 2020.
- Trends in Reinforcement Learning with Chelsea Finn
- Chelsea Finn. TWIML, 2020.
- Nan Jiang | TalkRL: The Reinforcement Learning Podcast
- Nan Jiang. TalkRL, 2020.
- Scott Fujimoto | TalkRL: The Reinforcement Learning Podcast
- Scott Fujimoto. TalkRL, 2019.
- CONSEQUENCES (RecSys 2023)
- Offline Reinforcement Learning (NeurIPS 2022)
- Reinforcement Learning for Real Life (NeurIPS 2022)
- CONSEQUENCES + REVEAL (RecSys 2022)
- Offline Reinforcement Learning (NeurIPS 2021)
- Reinforcement Learning for Real Life (ICML 2021)
- Reinforcement Learning Day 2021
- Offline Reinforcement Learning (NeurIPS 2020)
- Reinforcement Learning from Batch Data and Simulation
- Reinforcement Learning for Real Life (RL4RealLife 2020)
- Safety and Robustness in Decision Making (NeurIPS 2019)
- Reinforcement Learning for Real Life (ICML 2019)
- Real-world Sequential Decision Making (ICML 2019)
- Reinforcement Learning with Large Datasets: Robotics, Image Generation, and LLMs
- Sergey Levine. 2023.
- Counterfactual Evaluation and Learning for Interactive Systems
- Yuta Saito and Thorsten Joachims. KDD2022.
- Representation Learning for Online and Offline RL in Low-rank MDPs
- Masatoshi Uehara. RL Theory Seminar2022.
- Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
- Yunzong Xu. RL Theory Seminar2022.
- Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
- Kosuke Imai. Online Causal Inference Seminar2022.
- Deep Reinforcement Learning with Real-World Data
- Sergey Levine. 2022.
- Planning with Reinforcement Learning
- Sergey Levine. 2022.
- Imitation learning vs. offline reinforcement learning
- Sergey Levine. 2022.
- Tutorial on the Foundations of Offline Reinforcement Learning
- Romain Laroche and David Brandfonbrener. 2022.
- Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances [website]
- Yuta Saito and Thorstem Joachims. RecSys2021.
- Offline Reinforcement Learning
- Sergey Levine. BayLearn2021.
- Offline Reinforcement Learning
- Guy Tennenholtz. CHIL2021.
- Fast Rates for the Regret of Offline Reinforcement Learning
- Yichun Hu. RL Theory Seminar2021.
- Bellman-consistent Pessimism for Offline Reinforcement Learning
- Tengyan Xie. RL Theory Seminar2021.
- Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
- Masatoshi Uehara. RL Theory Seminar2021.
- Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
- Paria Rashidinejad. RL Theory Seminar2021.
- Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm
- Lin Chen. RL Theory Seminar2021.
- Is Pessimism Provably Efficient for Offline RL?
- Ying Jin. RL Theory Seminar2021.
- Adaptive Estimator Selection for Off-Policy Evaluation
- Yi Su. RL Theory Seminar2021.
- What are the Statistical Limits of Offline RL with Linear Function Approximation?
- Ruosong Wang. RL Theory Seminar2021.
- Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
- Andrea Zanette. RL Theory Seminar2021.
- A Gentle Introduction to Offline Reinforcement Learning
- Sergey Levine. 2021.
- Principles for Tackling Distribution Shift: Pessimism, Adaptation, and Anticipation
- Chelsea Finn. 2020-2021 Machine Learning Advances and Applications Seminar.
- Offline Reinforcement Learning: Incorporating Knowledge from Data into RL
- Sergey Levine. IJCAI-PRICAI2020 Knowledge Based Reinforcement Learning Workshop.
- Offline RL
- Nando de Freitas. NeurIPS2020 OfflineRL Workshop.
- Learning a Multi-Agent Simulator from Offline Demonstrations
- Brandyn White. NeurIPS2020 OfflineRL Workshop.
- Towards Reliable Validation and Evaluation for Offline RL
- Nan Jiang. NeurIPS2020 OfflineRL Workshop.
- Batch RL Models Built for Validation
- Finale Doshi-Velez. NeurIPS2020 OfflineRL Workshop.
- Offline Reinforcement Learning: From Algorithms to Practical Challenges
- Aviral Kumar and Sergey Levine. NeurIPS2020.
- Data Scalability for Robot Learning
- Chelsea Finn. RI Seminar2020.
- Statistically Efficient Offline Reinforcement Learning
- Nathan Kallus. ARL Seminor2020.
- Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning
- Yu-Xiang Wang. RL Theory Seminar2020.
- Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
- Mengdi Wang. RL Theory Seminar2020.
- Beyond the Training Distribution: Embodiment, Adaptation, and Symmetry
- Chelsea Finn. EI Seminar2020.
- Combining Statistical methods with Human Input for Evaluation and Optimization in Batch Settings
- Finale Doshi-Velez. NeurIPS2019 Workshop on Safety and Robustness in Decision Making.
- Efficiently Breaking the Curse of Horizon with Double Reinforcement Learning
- Nathan Kallus. NeurIPS2019 Workshop on Safety and Robustness in Decision Making.
- Scaling Probabilistically Safe Learning to Robotics
- Scott Niekum. NeurIPS2019 Workshop on Safety and Robustness in Decision Making.
- Deep Reinforcement Learning in the Real World
- Sergey Levine. Workshop on New Directions in Reinforcement Learning and Control2019.