“‘Imitation Learning’ Tag”,2019-12-20 (offline RL)⁠:

Bibliography for tag reinforcement-learning/imitation-learning, most recent first: 7 related tags, 132 annotations, & 13 links (parent).

• See Also
• Gwern
  • “GPT-3 Creative Fiction”, Gwern2020
  • “The Scaling Hypothesis”, Gwern2020
• Links
  • “A Revolution in How Robots Learn”
  • “Data Scaling Laws in Imitation Learning for Robotic Manipulation”, Lin et al 2024
  • “Motor Physics: Safety Implications of Geared Motors”, Jang2024
  • “GUI-WORLD: A Dataset for GUI-Oriented Multimodal LLM-Based Agents”, Chen et al 2024
  • “Earnings Call: Tesla Discusses Q1 2024 Challenges and AI Expansion”, Abdulkadir2024
  • “Beyond A^✱: Better Planning With Transformers via Search Dynamics Bootstrapping (Searchformer)”, Lehnert et al 2024
  • “Grandmaster-Level Chess Without Search”, Ruoss et al 2024
  • “Mobile ALOHA: Learning Bimanual Mobile Manipulation With Low-Cost Whole-Body Teleoperation”, Fu et al 2024
  • “Vision-Language Models As a Source of Rewards”, Baumli et al 2023
  • “Self-Supervised Behavior Cloned Transformers Are Path Crawlers for Text Games”, Wang & Jansen2023
  • “Learning Few-Shot Imitation As Cultural Transmission”, Bhoopchand et al 2023
  • “Calibrated Language Models Must Hallucinate”, Kalai & Vempala2023
  • “Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero”, Schut et al 2023
  • “Beyond Memorization: Violating Privacy Via Inference With Large Language Models”, Staab et al 2023
  • “ReST: Reinforced Self-Training (ReST) for Language Modeling”, Gulcehre et al 2023
  • “AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023
  • “Getting from Generative AI to Trustworthy AI: What LLMs Might Learn from Cyc”, Lenat & Marcus2023
  • “Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior”, Block et al 2023
  • “Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023
  • “GKD: Generalized Knowledge Distillation for Auto-Regressive Sequence Models”, Agarwal et al 2023
  • “ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023
  • “SequenceMatch: Imitation Learning for Autoregressive Sequence Modeling With Backtracking”, Cundy & Ermon2023
  • “Survival Instinct in Offline Reinforcement Learning”, Li et al 2023
  • “Thought Cloning: Learning to Think While Acting by Imitating Human Thinking”, Hu & Clune2023
  • “Let’s Verify Step by Step”, Lightman et al 2023
  • “The False Promise of Imitating Proprietary LLMs”, Gudibande et al 2023
  • “LIMA: Less Is More for Alignment”, Zhou et al 2023
  • “Revisiting the Minimalist Approach to Offline Reinforcement Learning”, Tarasov et al 2023
  • “ACT: Learning Fine-Grained Bimanual Manipulation With Low-Cost Hardware”, Zhao et al 2023
  • “MimicPlay: Long-Horizon Imitation Learning by Watching Human Play”, Wang et al 2023
  • “Toolformer: Language Models Can Teach Themselves to Use Tools”, Schick et al 2023
  • “Conditioning Predictive Models: Risks and Strategies”, Hubinger et al 2023
  • “Imitating Human Behavior With Diffusion Models”, Pearce et al 2023
  • “Solving Math Word Problems With Process & Outcome-Based Feedback”, Uesato et al 2022
  • “CICERO: Human-Level Play in the Game of Diplomacy by Combining Language Models With Strategic Reasoning”, Bakhtin et al 2022
  • “Token Turing Machines”, Ryoo 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
  • “Scaling Laws for Reward Model Overoptimization”, Gao et al 2022
  • “Human-AI Coordination via Human-Regularized Search and Learning”, Hu et al 2022
  • “Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization”, Ramamurthy et al 2022
  • “Nearest Neighbor Non-Autoregressive Text Generation”, Niwa et al 2022
  • “Generative Personas That Behave and Experience Like Humans”, Barthet et al 2022
  • “Diffusion-QL: Diffusion Policies As an Expressive Policy Class for Offline Reinforcement Learning”, Wang et al 2022
  • “Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022
  • “Improved Policy Optimization for Online Imitation Learning”, Lavington et al 2022
  • “Watch and Match: Supercharging Imitation With Regularized Optimal Transport”, Haldar 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
  • “Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022
  • “Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant et al 2022
  • “When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?”, Kumar et al 2022
  • “Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale”, Ramrakhya et al 2022
  • “Imitating, Fast and Slow: Robust Learning from Demonstrations via Decision-Time Planning”, Qi et al 2022
  • “Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning”, Valassakis et al 2022
  • “Inferring Rewards from Language in Context”, Lin et al 2022
  • “Robot Peels Banana With Goal-Conditioned Dual-Action Deep Imitation Learning”, Kim et al 2022
  • “The Unsurprising Effectiveness of Pre-Trained Vision Models for Control”, Parisi et al 2022
  • “VAPO: Affordance Learning from Play for Sample-Efficient Policy Learning”, Borja-Diaz et al 2022
  • “LID: Pre-Trained Language Models for Interactive Decision-Making”, Li et al 2022
  • “Conditional Imitation Learning for Multi-Agent Games”, Shih et al 2022
  • “Amortized Noisy Channel Neural Machine Translation”, Pang et al 2021
  • “WebGPT: Browser-Assisted Question-Answering With Human Feedback”, Nakano et al 2021
  • “Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Jacob et al 2021
  • “JueWu-MC: Playing Minecraft With Sample-Efficient Hierarchical Reinforcement Learning”, Lin et al 2021
  • “A General Language Assistant As a Laboratory for Alignment”, Askell et al 2021
  • “AW-Opt: Learning Robotic Skills With Imitation and Reinforcement at Scale”, Lu et al 2021
  • “RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning”, Ramos et al 2021
  • “BC-Z: Zero-Shot Task Generalization With Robotic Imitation Learning”, Jang et al 2021
  • “Is Bang-Bang Control All You Need? Solving Continuous Control With Bernoulli Policies”, Seyde et al 2021
  • “SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies”, Vitelli et al 2021
  • “TrufLL: Learning Natural Language Generation from Scratch”, Donati et al 2021
  • “Relating Neural Text Degeneration to Exposure Bias”, Chiang & Chen2021
  • “Learning to Navigate Sidewalks in Outdoor Environments”, Sorokin et al 2021
  • “PlaTe: Visually-Grounded Planning With Transformers in Procedural Tasks”, Sun et al 2021
  • “Implicit Behavioral Cloning”, Florence et al 2021
  • “DexMV: Imitation Learning for Dexterous Manipulation from Human Videos”, Qin et al 2021
  • “Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs”, Sonnerat et al 2021
  • “A Minimalist Approach to Offline Reinforcement Learning”, Fujimoto & Gu2021
  • “Hyperparameter Selection for Imitation Learning”, Hussenot et al 2021
  • “From Motor Control to Team Play in Simulated Humanoid Football”, Liu et al 2021
  • “On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning”, Vischer et al 2021
  • “Counter-Strike Deathmatch With Large-Scale Behavioral Cloning”, Pearce & Zhu2021
  • “Fully General Online Imitation Learning”, Cohen et al 2021
  • “The MineRL 2020 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2021
  • “Meta Learning Backpropagation And Improving It”, Kirsch & Schmidhuber2020
  • “SCC: an Efficient Deep Reinforcement Learning Agent Mastering the Game of StarCraft II”, Wang et al 2020
  • “Imitating Interactive Intelligence”, Abramson et al 2020
  • “TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game”, Han et al 2020
  • “RetinaGAN: An Object-Aware Approach to Sim-To-Real Transfer”, Ho et al 2020
  • “Emergent Social Learning via Multi-Agent Reinforcement Learning”, Ndousse et al 2020
  • “Automatic Discovery of Interpretable Planning Strategies”, Skirzyński et al 2020
  • “Learning Agile Robotic Locomotion Skills by Imitating Animals”, Peng et al 2020
  • “Reinforcement Learning for Combinatorial Optimization: A Survey”, Mazyavkina et al 2020
  • “Bayesian REX: Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences”, Brown et al 2020
  • “AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch”, Knight2020
  • “Deep Bayesian Reward Learning from Preferences”, Brown & Niekum2019
  • “Learning Norms from Stories: A Prior for Value Aligned Agents”, Frazier et al 2019
  • “Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?”, Du et al 2019
  • “Learning to Reason in Large Theories without Imitation”, Bansal et al 2019
  • “The MineRL 2019 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2019
  • “Go-Explore: a New Approach for Hard-Exploration Problems”, Ecoffet et al 2019
  • “Hierarchical Reinforcement Learning for Multi-Agent MOBA Game”, Zhang et al 2019
  • “ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst”, Bansal et al 2018
  • “Reward Learning from Human Preferences and Demonstrations in Atari”, Ibarz et al 2018
  • “Language GANs Falling Short”, Caccia et al 2018
  • “Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow”, Peng et al 2018
  • “Human-Like Playtesting With Deep Learning”, Gudmundsson et al 2018
  • “Convergence of Value Aggregation for Imitation Learning”, Cheng & Boots2018
  • “Policy Optimization by Genetic Distillation”, Gangwani & Peng2017
  • “Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, Sabatelli2017 (page 3)
  • “DropoutDAgger: A Bayesian Approach to Safe Imitation Learning”, Menda et al 2017
  • “One-Shot Visual Imitation Learning via Meta-Learning”, Finn et al 2017
  • “Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”, Rahmatizadeh et al 2017
  • “Learning Human Behaviors from Motion Capture by Adversarial Imitation”, Merel et al 2017
  • “Grammatical Error Correction With Neural Reinforcement Learning”, Sakaguchi et al 2017
  • “Path Integral Networks: End-To-End Differentiable Optimal Control”, Okada et al 2017
  • “Gated-Attention Architectures for Task-Oriented Language Grounding”, Chaplot et al 2017
  • “Visual Semantic Planning Using Deep Successor Representations”, Zhu et al 2017
  • “A Deep Reinforced Model for Abstractive Summarization”, Paulus et al 2017
  • “One-Shot Imitation Learning”, Duan et al 2017
  • “Model-Based Adversarial Imitation Learning”, Baram et al 2016
  • “A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models”, Finn et al 2016
  • “SeqGAN: Sequence Generative Adversarial Nets With Policy Gradient”, Yu et al 2016
  • “Generative Adversarial Imitation Learning”, Ho & Ermon2016
  • “Mastering the Game of Go With Deep Neural Networks and Tree Search”, Silver et al 2016
  • “An Invitation to Imitation”, Bagnell2015
  • “DAgger: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”, Ross et al 2010
  • “The Hidden Structure of Overimitation”, Lyons et al 2007
  • “Google DeepMind’s Grandmaster-Level Chess Without Search”
  • “Language Models Model Us”
  • “Sony’s Racing Car AI Just Destroyed Its Human Competitors—By Being Nice (and Fast)”
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