“‘Decision Transformer’ Tag”,2021-03-26 (discrete diffusion model, instruct-tuning LLMs, AI scaling, imitation learning, offline RL, preference learning, RL scaling, truesight (stylometrics); backlinks)⁠:

Bibliography for tag reinforcement-learning/model/decision-transformer, most recent first: 4 related tags, 53 annotations, & 23 links (parent).

• See Also
• Links
  • “Diffusion Forcing: Next-Token Prediction Meets Full-Sequence Diffusion”, Chen et al 2024
  • “Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models”, Karvonen2024
  • “A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task”, Brinkmann et al 2024
  • “Robust Agents Learn Causal World Models”, Richens & Everitt2024
  • “Diff History for Neural Language Agents”, Piterbarg et al 2023
  • “Responsibility & Safety: Our Approach”, DeepMind2023
  • “Diversifying AI: Towards Creative Chess With AlphaZero (AZ_db)”, Zahavy et al 2023
  • “PASTA: Pretrained Action-State Transformer Agents”, Boige et al 2023
  • “Supervised Pretraining Can Learn In-Context Reinforcement Learning”, Lee et al 2023
  • “Direct Preference Optimization (DPO): Your Language Model Is Secretly a Reward Model”, Rafailov et al 2023
  • “Think Before You Act: Unified Policy for Interleaving Language Reasoning With Actions”, Mezghani et al 2023
  • “Learning Humanoid Locomotion With Transformers”, Radosavovic et al 2023
  • “Pretraining Language Models With Human Preferences”, Korbak et al 2023
  • “Conditioning Predictive Models: Risks and Strategies”, Hubinger et al 2023
  • “Language Models As Agent Models”, Andreas2022
  • “In-Context Reinforcement Learning With Algorithm Distillation”, Laskin et al 2022
  • “Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task”, Li et al 2022
  • “g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, Peebles et al 2022
  • “Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, Jiang et al 2022
  • “Goal-Conditioned Generators of Deep Policies”, Faccio et al 2022
  • “Demis Hassabis: DeepMind—AI, Superintelligence & the Future of Humanity § Turing Test”, Hassabis & Fridman2022
  • “Prompting Decision Transformer for Few-Shot Policy Generalization”, Xu et al 2022
  • “Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022
  • “When Does Return-Conditioned Supervised Learning Work for Offline Reinforcement Learning?”, Brandfonbrener et al 2022
  • “You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments”, Paster et al 2022
  • “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Wen et al 2022
  • “Multi-Game Decision Transformers”, Lee et al 2022
  • “Quark: Controllable Text Generation With Reinforced Unlearning”, Lu et al 2022
  • “Planning With Diffusion for Flexible Behavior Synthesis”, Janner et al 2022
  • “Gato: A Generalist Agent”, Reed et al 2022
  • “Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?”, Cui et al 2022
  • “All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL”, Arulkumaran et al 2022
  • “Learning Relative Return Policies With Upside-Down Reinforcement Learning”, Ashley et al 2022
  • “NeuPL: Neural Population Learning”, Liu et al 2022
  • “ODT: Online Decision Transformer”, Zheng et al 2022
  • “Jury Learning: Integrating Dissenting Voices into Machine Learning Models”, Gordon et al 2022
  • “Can Wikipedia Help Offline Reinforcement Learning?”, Reid et al 2022
  • “In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, Kurin et al 2022
  • “Offline Pre-Trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Meng et al 2021
  • “Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, Ortega et al 2021
  • “Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, Janner et al 2021
  • “Decision Transformer: Reinforcement Learning via Sequence Modeling”, Chen et al 2021
  • “Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, Alcorn & Nguyen2021
  • “The Go Transformer: Natural Language Modeling for Game Play”, Ciolino et al 2020
  • “Transformers Play Chess”, Cheng2020
  • “A Very Unlikely Chess Game”, Alexander2020
  • “Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, Schmidhuber2019
  • “Training Agents Using Upside-Down Reinforcement Learning (UDRL)”, Srivastava et al 2019
  • “Reward Hacking Behavior Can Generalize across Tasks”
  • “Evidence of Learned Look-Ahead in a Chess-Playing Neural Network”
  • “Interview With Robert Kralisch on Simulators”
  • “TalkRL: The Reinforcement Learning Podcast: Aravind Srinivas 2: Aravind Srinivas, Research Scientist at OpenAI, Returns to Talk Decision Transformer, VideoGPT, Choosing Problems, and Explore vs Exploit in Research Careers”
  • “Supplementary Video for Do As I Can, Not As I Say: Grounding Language in Robotic Affordances”
  • Sort By Magic
    • generative-models decision-transfer multi-agent transformer-interaction robot-manipulation decision-transforming
    • chess-ai language-reward reinforcement-creation decision-training natural-language
    • trajectory-synthesis decision-planning sequence-modeling multi-agent diffusion-planning trajectory-transformer
    • upside-down-rl
• Miscellaneous
• Bibliography

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