cluster #7

Papers

• Transfer Learning In Deep Reinforcement Learning: A Survey (2020)
  Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain, et al.
  20.93
• Using Human Feedback To Fine-tune Diffusion Models Without Any Reward Model (2023)
  Kai Yang, Jian Tao, Jiafei Lyu, et al.
  17.39
• Goal-conditioned Imitation Learning Using Score-based Diffusion Policies (2023)
  Moritz Reuss, Maximilian Li, Xiaogang Jia, et al.
  13.74
• Data-efficient Domain Randomization With Bayesian Optimization (2020)
  Fabio Muratore, Christian Eilers, Michael Gienger, et al.
  13.28
• Overcoming Model Bias For Robust Offline Deep Reinforcement Learning (2020)
  Phillip Swazinna, Steffen Udluft, Thomas Runkler
  11.58
• Pessimistic Value Iteration For Multi-task Data Sharing In Offline Reinforcement Learning (2024)
  Chenjia Bai, Lingxiao Wang, Jianye Hao, et al.
  9.33
• Don't Start From Scratch: Behavioral Refinement Via Interpolant-based Policy Diffusion (2024)
  Kaiqi Chen, Eugene Lim, Kelvin Lin, et al.
  9.28
• Map-based Experience Replay: A Memory-efficient Solution To Catastrophic Forgetting In Reinforcement Learning (2023)
  Muhammad Burhan Hafez, Tilman Immisch, Tom Weber, et al.
  9.23
• Imitation Learning Of Neural Spatio-temporal Point Processes (2019)
  Shixiang Zhu, Shuang Li, Zhigang Peng, et al.
  9.23
• Interactive Reinforcement Learning With Dynamic Reuse Of Prior Knowledge From Human/agent's Demonstration (2018)
  Zhaodong Wang, Matthew E. Taylor
  8.60
• Predictive Representations: Building Blocks Of Intelligence (2024)
  Wilka Carvalho, Momchil S. Tomov, William de Cothi, et al.
  8.09
• 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
• MULTIPOLAR: Multi-source Policy Aggregation For Transfer Reinforcement Learning Between Diverse Environmental Dynamics (2019)
  Mohammadamin Barekatain, Ryo Yonetani, Masashi Hamaya
  7.81
• A Perspective Of Q-value Estimation On Offline-to-online Reinforcement Learning (2023)
  Yinmin Zhang, Jie Liu, Chuming Li, et al.
  7.81
• A Definition Of Continual Reinforcement Learning (2023)
  David Abel, André Barreto, Benjamin van Roy, et al.
  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
• Adversarial Imitation Learning Via Random Search (2020)
  Myungjae Shin, Joongheon Kim
  7.16
• Dynamics-adaptive Continual Reinforcement Learning Via Progressive Contextualization (2022)
  Tiantian Zhang, Zichuan Lin, Yuxing Wang, et al.
  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
• Trajectory-wise Multiple Choice Learning For Dynamics Generalization In Reinforcement Learning (2020)
  Younggyo Seo, Kimin Lee, Ignasi Clavera, et al.
  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
• Revisiting Design Choices In Offline Model-based Reinforcement Learning (2021)
  Cong Lu, Philip J. Ball, Jack Parker-Holder, et al.
  6.34
• AKF-SR: Adaptive Kalman Filtering-based Successor Representation (2022)
  Parvin Malekzadeh, Mohammad Salimibeni, Ming Hou, et al.
  6.34
• Temple: Learning Template Of Transitions For Sample Efficient Multi-task RL (2020)
  Yanchao Sun, Xiangyu Yin, Furong Huang
  6.34
• Successor Feature Sets: Generalizing Successor Representations Across Policies (2021)
  Kianté Brantley, Soroush Mehri, Geoffrey J. Gordon
  5.84
• A Joint Imitation-reinforcement Learning Framework For Reduced Baseline Regret (2022)
  Sheelabhadra Dey, Sumedh Pendurkar, Guni Sharon, et al.
  5.84
• Causal Transfer For Imitation Learning And Decision Making Under Sensor-shift (2020)
  Jalal Etesami, Philipp Geiger
  5.84
• Overcoming The Sim-to-real Gap: Leveraging Simulation To Learn To Explore For Real-world RL (2024)
  Andrew Wagenmaker, Kevin Huang, Liyiming Ke, 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
• Diffusion Models For Reinforcement Learning: A Survey (2023)
  Zhengbang Zhu, Hanye Zhao, Haoran He, et al.
  5.64
• 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
• IOB: Integrating Optimization Transfer And Behavior Transfer For Multi-policy Reuse (2023)
  Siyuan Li, Hao Li, Jin Zhang, et al.
  5.24
• Uncertainty-aware Transfer Across Tasks Using Hybrid Model-based Successor Feature Reinforcement Learning (2023)
  Parvin Malekzadeh, Ming Hou, Konstantinos N. Plataniotis
  5.24
• DEALIO: Data-efficient Adversarial Learning For Imitation From Observation (2021)
  Faraz Torabi, Garrett Warnell, Peter Stone
  5.24
• Towards Robust Offline-to-online Reinforcement Learning Via Uncertainty And Smoothness (2023)
  Xiaoyu Wen, Xudong Yu, Rui Yang, et al.
  5.24
• Cohesion-based Online Actor-critic Reinforcement Learning For Mhealth Intervention (2017)
  Feiyun Zhu, Peng Liao, Xinliang Zhu, et al.
  5.24
• TD3 With Reverse KL Regularizer For Offline Reinforcement Learning From Mixed Datasets (2022)
  Yuanying Cai, Chuheng Zhang, Li Zhao, et al.
  4.52
• S-TRIGGER: Continual State Representation Learning Via Self-triggered Generative Replay (2019)
  Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
  4.52
• Did We Personalize? Assessing Personalization By An Online Reinforcement Learning Algorithm Using Resampling (2023)
  Susobhan Ghosh, Raphael Kim, Prasidh Chhabria, 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
• Adaflow: Imitation Learning With Variance-adaptive Flow-based Policies (2024)
  Xixi Hu, Bo Liu, Xingchao Liu, 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
• Residual Q-learning: Offline And Online Policy Customization Without Value (2023)
  Chenran Li, Chen Tang, Haruki Nishimura, 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