“‘Decision Transformer’ Tag”,2021-03-26 (; backlinks):
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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”, et al 2024
- “Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models”, 2024
- “A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task”, et al 2024
- “Robust Agents Learn Causal World Models”, 2024
- “Diff History for Neural Language Agents”, et al 2023
- “Responsibility & Safety: Our Approach”, Deep2023
- “Diversifying AI: Towards Creative Chess With AlphaZero (AZdb)”, et al 2023
- “PASTA: Pretrained Action-State Transformer Agents”, et al 2023
- “Supervised Pretraining Can Learn In-Context Reinforcement Learning”, et al 2023
- “Direct Preference Optimization (DPO): Your Language Model Is Secretly a Reward Model”, et al 2023
- “Think Before You Act: Unified Policy for Interleaving Language Reasoning With Actions”, et al 2023
- “Learning Humanoid Locomotion With Transformers”, et al 2023
- “Pretraining Language Models With Human Preferences”, et al 2023
- “Conditioning Predictive Models: Risks and Strategies”, et al 2023
- “Language Models As Agent Models”, 2022
- “In-Context Reinforcement Learning With Algorithm Distillation”, et al 2022
- “Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task”, et al 2022
- “
g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, et al 2022- “Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, et al 2022
- “Goal-Conditioned Generators of Deep Policies”, et al 2022
- “Demis Hassabis: DeepMind—AI, Superintelligence & the Future of Humanity § Turing Test”, 2022
- “Prompting Decision Transformer for Few-Shot Policy Generalization”, et al 2022
- “Boosting Search Engines With Interactive Agents”, et al 2022
- “When Does Return-Conditioned Supervised Learning Work for Offline Reinforcement Learning?”, et al 2022
- “You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments”, et al 2022
- “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, et al 2022
- “Multi-Game Decision Transformers”, et al 2022
- “Quark: Controllable Text Generation With Reinforced Unlearning”, et al 2022
- “Planning With Diffusion for Flexible Behavior Synthesis”, et al 2022
- “Gato: A Generalist Agent”, et al 2022
- “Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?”, et al 2022
- “All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL”, et al 2022
- “Learning Relative Return Policies With Upside-Down Reinforcement Learning”, et al 2022
- “NeuPL: Neural Population Learning”, et al 2022
- “ODT: Online Decision Transformer”, et al 2022
- “Jury Learning: Integrating Dissenting Voices into Machine Learning Models”, et al 2022
- “Can Wikipedia Help Offline Reinforcement Learning?”, et al 2022
- “In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, et al 2022
- “Offline Pre-Trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, et al 2021
- “Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, et al 2021
- “Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, et al 2021
- “Decision Transformer: Reinforcement Learning via Sequence Modeling”, et al 2021
- “Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, 2021
- “The Go Transformer: Natural Language Modeling for Game Play”, et al 2020
- “Transformers Play Chess”, 2020
- “A Very Unlikely Chess Game”, 2020
- “Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, 2019
- “Training Agents Using Upside-Down Reinforcement Learning (UDRL)”, 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”
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generative-models decision-transfer multi-agent transformer-interaction robot-manipulation decision-transformingchess-ai language-reward reinforcement-creation decision-training natural-languagetrajectory-synthesis decision-planning sequence-modeling multi-agent diffusion-planning trajectory-transformerupside-down-rl- Miscellaneous
- Bibliography