“‘Offline RL’ Tag”,2020-05-04 (imitation learning, Decision Transformer, MuZero, MARL, preference learning, robotics)⁠:

Bibliography for tag reinforcement-learning/offline, most recent first: 3 related tags, 52 annotations, & 10 links (parent).

• See Also
• Links
  • “Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data”, Tajwar et al 2024
  • “Dataset Reset Policy Optimization for RLHF”, Chang et al 2024
  • “Mastering Stacking of Diverse Shapes With Large-Scale Iterative Reinforcement Learning on Real Robots”, Lampe et al 2023
  • “Vision-Language Models As a Source of Rewards”, Baumli et al 2023
  • “Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReST^EM)”, Singh et al 2023
  • “Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations”, Hong et al 2023
  • “Course Correcting Koopman Representations”, Fathi et al 2023
  • “Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions”, Chebotar et al 2023
  • “Subwords As Skills: Tokenization for Sparse-Reward Reinforcement Learning”, Yunis et al 2023
  • “What Are Dreams For? Converging Lines of Research Suggest That We Might Be Misunderstanding Something We Do Every Night of Our Lives”, Gefter2023
  • “ReST: Reinforced Self-Training (ReST) for Language Modeling”, Gulcehre et al 2023
  • “AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023
  • “Learning to Model the World With Language”, Lin et al 2023
  • “Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior”, Block et al 2023
  • “PASTA: Pretrained Action-State Transformer Agents”, Boige et al 2023
  • “Fighting Uncertainty With Gradients: Offline Reinforcement Learning via Diffusion Score Matching”, Suh et al 2023
  • “Twitching in Sensorimotor Development from Sleeping Rats to Robots”, Blumberg et al 2023
  • “Survival Instinct in Offline Reinforcement Learning”, Li et al 2023
  • “BetaZero: Belief-State Planning for Long-Horizon POMDPs Using Learned Approximations”, Moss et al 2023
  • “Improving Language Models With Advantage-Based Offline Policy Gradients”, Baheti et al 2023
  • “Revisiting the Minimalist Approach to Offline Reinforcement Learning”, Tarasov et al 2023
  • “Think Before You Act: Unified Policy for Interleaving Language Reasoning With Actions”, Mezghani et al 2023
  • “Off-The-Grid MARL (OG-MARL): Datasets With Baselines for Offline Multi-Agent Reinforcement Learning”, Formanek et al 2023
  • “Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes”, Kumar et al 2022
  • “Dungeons and Data: A Large-Scale NetHack Dataset”, Hambro et al 2022
  • “In-Context Reinforcement Learning With Algorithm Distillation”, Laskin et al 2022
  • “CORL: Research-Oriented Deep Offline Reinforcement Learning Library”, Tarasov et al 2022
  • “Diffusion-QL: Diffusion Policies As an Expressive Policy Class for Offline Reinforcement Learning”, Wang et al 2022
  • “Offline RL Policies Should Be Trained to Be Adaptive”, Ghosh et al 2022
  • “Prompting Decision Transformer for Few-Shot Policy Generalization”, Xu et al 2022
  • “Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”, Baker et al 2022
  • “Large-Scale Retrieval for Reinforcement Learning”, Humphreys et al 2022
  • “Offline RL for Natural Language Generation With Implicit Language Q Learning”, Snell et al 2022
  • “When Does Return-Conditioned Supervised Learning Work for Offline Reinforcement Learning?”, Brandfonbrener et al 2022
  • “Newton’s Method for Reinforcement Learning and Model Predictive Control”, Bertsekas2022
  • “You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments”, Paster et al 2022
  • “Multi-Game Decision Transformers”, Lee et al 2022
  • “When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?”, Kumar et al 2022
  • “Don’t Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning (ExORL)”, Yarats et al 2022
  • “Offline Pre-Trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Meng et al 2021
  • “A Workflow for Offline Model-Free Robotic Reinforcement Learning”, Kumar et al 2021
  • “Conservative Objective Models for Effective Offline Model-Based Optimization”, Trabucco et al 2021
  • “A Minimalist Approach to Offline Reinforcement Learning”, Fujimoto & Gu2021
  • “Is Pessimism Provably Efficient for Offline RL?”, Jin et al 2020
  • “What Are the Statistical Limits of Offline RL With Linear Function Approximation?”, Wang et al 2020
  • “MOPO: Model-Based Offline Policy Optimization”, Yu et al 2020
  • “Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems”, Levine et al 2020
  • “D4RL: Datasets for Deep Data-Driven Reinforcement Learning”, Fu et al 2020
  • “Q^✱ Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison”, Xie & Jiang2020
  • “Scaling Data-Driven Robotics With Reward Sketching and Batch Reinforcement Learning”, Cabi et al 2019
  • “QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation”, Kalashnikov et al 2018
  • “The Netflix Recommender System”, Gomez-Uribe & Hunt2015
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