Offline RL

Papers

• Meta-reinforcement Learning For The Tuning Of PI Controllers: An Offline Approach (2022)
  Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom, et al.
  12.02
• Overcoming Model Bias For Robust Offline Deep Reinforcement Learning (2020)
  Phillip Swazinna, Steffen Udluft, Thomas Runkler
  11.58
• Offline Reinforcement Learning For Wireless Network Optimization With Mixture Datasets (2023)
  Kun Yang, Cong Shen, Jing Yang, et al.
  9.59
• Pessimistic Value Iteration For Multi-task Data Sharing In Offline Reinforcement Learning (2024)
  Chenjia Bai, Lingxiao Wang, Jianye Hao, et al.
  9.33
• Adaptive Behavior Cloning Regularization For Stable Offline-to-online Reinforcement Learning (2022)
  Yi Zhao, Rinu Boney, Alexander Ilin, et al.
  8.09
• Diffusion Policies Creating A Trust Region For Offline Reinforcement Learning (2024)
  Tianyu Chen, Zhendong Wang, Mingyuan Zhou
  8.04
• A Perspective Of Q-value Estimation On Offline-to-online Reinforcement Learning (2023)
  Yinmin Zhang, Jie Liu, Chuming Li, et al.
  7.81
• Offline Decentralized Multi-agent Reinforcement Learning (2021)
  Jiechuan Jiang, Zongqing Lu
  7.50
• Hundreds Guide Millions: Adaptive Offline Reinforcement Learning With Expert Guidance (2023)
  Qisen Yang, Shenzhi Wang, Qihang Zhang, et al.
  7.50
• AD4RL: Autonomous Driving Benchmarks For Offline Reinforcement Learning With Value-based Dataset (2024)
  Dongsu Lee, Chanin Eom, Minhae Kwon
  7.16
• Using Offline Data To Speed Up Reinforcement Learning In Procedurally Generated Environments (2023)
  Alain Andres, Lukas Schäfer, Stefano V. Albrecht, et al.
  6.77
• Why So Pessimistic? Estimating Uncertainties For Offline RL Through Ensembles, And Why Their Independence Matters (2022)
  Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum
  6.77
• Offline Imitation Learning With Suboptimal Demonstrations Via Relaxed Distribution Matching (2023)
  Lantao Yu, Tianhe Yu, Jiaming Song, et al.
  6.77
• GTA: Generative Trajectory Augmentation With Guidance For Offline Reinforcement Learning (2024)
  Jaewoo Lee, Sujin Yun, Taeyoung Yun, et al.
  6.62
• BAFFLE: Hiding Backdoors In Offline Reinforcement Learning Datasets (2022)
  Chen Gong, Zhou Yang, Yunpeng Bai, et al.
  6.34
• Revisiting Design Choices In Offline Model-based Reinforcement Learning (2021)
  Cong Lu, Philip J. Ball, Jack Parker-Holder, et al.
  6.34
• Model-based Offline Quantum Reinforcement Learning (2024)
  Simon Eisenmann, Daniel Hein, Steffen Udluft, et al.
  5.84
• Offline Multi-agent Reinforcement Learning With Implicit Global-to-local Value Regularization (2023)
  Xiangsen Wang, Haoran Xu, Yinan Zheng, et al.
  5.84
• Decoupled Prioritized Resampling For Offline RL (2023)
  Yang Yue, Bingyi Kang, Xiao Ma, et al.
  5.84
• Balancing Policy Constraint And Ensemble Size In Uncertainty-based Offline Reinforcement Learning (2023)
  Alex Beeson, Giovanni Montana
  5.24
• POPO: Pessimistic Offline Policy Optimization (2020)
  Qiang He, Xinwen Hou
  5.24
• Learning From Good Trajectories In Offline Multi-agent Reinforcement Learning (2022)
  Qi Tian, Kun Kuang, Furui Liu, et al.
  5.24
• Towards Robust Offline-to-online Reinforcement Learning Via Uncertainty And Smoothness (2023)
  Xiaoyu Wen, Xudong Yu, Rui Yang, et al.
  5.24
• Compositional Transduction with Latent Analogies for Offline Goal-Conditioned Reinforcement Learning (2026)
  Junseok Kim et al.
  4.54
• How Much Online RL is Enough? Informative Rollouts for Offline Preference Optimization in RLVR (2026)
  Richa Verma et al.
  4.54
• TD3 With Reverse KL Regularizer For Offline Reinforcement Learning From Mixed Datasets (2022)
  Yuanying Cai, Chuheng Zhang, Li Zhao, et al.
  4.52
• A Simple Unified Uncertainty-guided Framework For Offline-to-online Reinforcement Learning (2023)
  Siyuan Guo, Yanchao Sun, Jifeng Hu, et al.
  4.52
• Guided Online Distillation: Promoting Safe Reinforcement Learning By Offline Demonstration (2023)
  Jinning Li, Xinyi Liu, Banghua Zhu, et al.
  4.52
• S2P: State-conditioned Image Synthesis For Data Augmentation In Offline Reinforcement Learning (2022)
  Daesol Cho, Dongseok Shim, H. Jin Kim
  3.58
• OER: Offline Experience Replay For Continual Offline Reinforcement Learning (2023)
  Sibo Gai, Donglin Wang, Li He
  3.58
• Expert Q-learning: Deep Reinforcement Learning With Coarse State Values From Offline Expert Examples (2021)
  Li Meng, Anis Yazidi, Morten Goodwin, et al.
  3.58
• Towards Robust Policy: Enhancing Offline Reinforcement Learning With Adversarial Attacks And Defenses (2024)
  Thanh Nguyen, Tung M. Luu, Tri Ton, et al.
  3.58
• One Risk To Rule Them All: A Risk-sensitive Perspective On Model-based Offline Reinforcement Learning (2022)
  Marc Rigter, Bruno Lacerda, Nick Hawes
  3.58
• Flow To Control: Offline Reinforcement Learning With Lossless Primitive Discovery (2022)
  Yiqin Yang, Hao Hu, Wenzhe Li, et al.
  3.58
• Diverse Randomized Value Functions: A Provably Pessimistic Approach For Offline Reinforcement Learning (2024)
  Xudong Yu, Chenjia Bai, Hongyi Guo, et al.
  3.58
• Federated Offline Policy Optimization With Dual Regularization (2024)
  Sheng Yue, Zerui Qin, Xingyuan Hua, et al.
  3.58
• Entropy-regularized Diffusion Policy With Q-ensembles For Offline Reinforcement Learning (2024)
  Ruoqi Zhang, Ziwei Luo, Jens Sjölund, et al.
  3.58
• Train Once, Get A Family: State-adaptive Balances For Offline-to-online Reinforcement Learning (2023)
  Shenzhi Wang, Qisen Yang, Jiawei Gao, et al.
  3.25
• Adaptive Advantage-guided Policy Regularization For Offline Reinforcement Learning (2024)
  Tenglong Liu, Yang Li, Yixing Lan, et al.
  3.09
• Diffusion Actor-critic: Formulating Constrained Policy Iteration As Diffusion Noise Regression For Offline Reinforcement Learning (2024)
  Linjiajie Fang, Ruoxue Liu, Jing Zhang, et al.
  2.92
• Beyond Uniform Sampling: Offline Reinforcement Learning With Imbalanced Datasets (2023)
  Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, et al.
  2.83
• Offline Retraining For Online RL: Decoupled Policy Learning To Mitigate Exploration Bias (2023)
  Max Sobol Mark, Archit Sharma, Fahim Tajwar, et al.
  2.56
• Look Beneath The Surface: Exploiting Fundamental Symmetry For Sample-efficient Offline RL (2023)
  Peng Cheng, Xianyuan Zhan, Zhihao Wu, et al.
  2.26
• Off-the-grid MARL: Datasets With Baselines For Offline Multi-agent Reinforcement Learning (2023)
  Claude Formanek, Asad Jeewa, Jonathan Shock, et al.
  2.26
• Fast Rates For The Regret Of Offline Reinforcement Learning (2021)
  Yichun Hu, Nathan Kallus, Masatoshi Uehara
  2.26
• Iteratively Refined Behavior Regularization For Offline Reinforcement Learning (2023)
  Xiaohan Hu, Yi Ma, Chenjun Xiao, et al.
  2.26
• On The Sample Complexity Of Vanilla Model-based Offline Reinforcement Learning With Dependent Samples (2023)
  Mustafa O. Karabag, Ufuk Topcu
  2.26
• A Benchmark Environment For Offline Reinforcement Learning In Racing Games (2024)
  Girolamo MacAluso, Alessandro Sestini, Andrew D. Bagdanov
  2.26
• Improving Zero-shot Generalization In Offline Reinforcement Learning Using Generalized Similarity Functions (2021)
  Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, et al.
  2.26
• Cal-ql: Calibrated Offline RL Pre-training For Efficient Online Fine-tuning (2023)
  Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, et al.
  2.26