- See Also
-
Links
- “Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models”, Karvonen 2024
- “Robust Agents Learn Causal World Models”, Richens & Everitt 2024
- “Diversifying AI: Towards Creative Chess With AlphaZero (AZdb)”, Zahavy et al 2023
- “PASTA: Pretrained Action-State Transformer Agents”, Boige 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”, Andreas 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 & Fridman 2022
- “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 & Nguyen 2021
- “The Go Transformer: Natural Language Modeling for Game Play”, Ciolino et al 2020
- “Transformers Play Chess”, Cheng 2020
- “A Very Unlikely Chess Game”, Alexander 2020
- “Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, Schmidhuber 2019
- “Training Agents Using Upside-Down Reinforcement Learning (UDRL)”, Srivastava et al 2019
- “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
- Miscellaneous
- Link Bibliography
See Also
Links
“Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models”, Karvonen 2024
Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models
“Robust Agents Learn Causal World Models”, Richens & Everitt 2024
“Diversifying AI: Towards Creative Chess With AlphaZero (AZdb)”, Zahavy et al 2023
Diversifying AI: Towards Creative Chess with AlphaZero (AZdb)
“PASTA: Pretrained Action-State Transformer Agents”, Boige et al 2023
“Direct Preference Optimization (DPO): Your Language Model Is Secretly a Reward Model”, Rafailov et al 2023
Direct Preference Optimization (DPO): Your Language Model is Secretly a Reward Model
“Think Before You Act: Unified Policy for Interleaving Language Reasoning With Actions”, Mezghani et al 2023
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions
“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”, Andreas 2022
“g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, Peebles et al 2022
g.pt: Learning to Learn with Generative Models of Neural Network Checkpoints
“Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, Jiang et al 2022
Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space
“Goal-Conditioned Generators of Deep Policies”, Faccio et al 2022
“Demis Hassabis: DeepMind—AI, Superintelligence & the Future of Humanity § Turing Test”, Hassabis & Fridman 2022
Demis Hassabis: DeepMind—AI, Superintelligence & the Future of Humanity § Turing Test
“Prompting Decision Transformer for Few-Shot Policy Generalization”, Xu et al 2022
Prompting Decision Transformer for Few-Shot Policy Generalization
“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
When does return-conditioned supervised learning work for offline reinforcement learning?
“You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments”, Paster et al 2022
You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments
“MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Wen et al 2022
MAT: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
“Multi-Game Decision Transformers”, Lee et al 2022
“Quark: Controllable Text Generation With Reinforced Unlearning”, Lu et al 2022
Quark: Controllable Text Generation with Reinforced Unlearning
“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
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
“All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL”, Arulkumaran et al 2022
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
“Learning Relative Return Policies With Upside-Down Reinforcement Learning”, Ashley et al 2022
Learning Relative Return Policies With Upside-Down Reinforcement Learning
“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
Jury Learning: Integrating Dissenting Voices into Machine Learning Models
“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
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
“Offline Pre-Trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Meng et al 2021
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
“Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, Ortega et al 2021
Shaking the foundations: delusions in sequence models for interaction and control
“Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, Janner et al 2021
Trajectory Transformer: Reinforcement Learning as One Big Sequence Modeling Problem
“Decision Transformer: Reinforcement Learning via Sequence Modeling”, Chen et al 2021
Decision Transformer: Reinforcement Learning via Sequence Modeling
“Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, Alcorn & Nguyen 2021
baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents
“The Go Transformer: Natural Language Modeling for Game Play”, Ciolino et al 2020
“Transformers Play Chess”, Cheng 2020
“A Very Unlikely Chess Game”, Alexander 2020
“Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, Schmidhuber 2019
Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions
“Training Agents Using Upside-Down Reinforcement Learning (UDRL)”, Srivastava et al 2019
Training Agents using Upside-Down Reinforcement Learning (UDRL)
“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”
Supplementary video for Do As I Can, Not As I Say: Grounding Language in Robotic Affordances:
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
planning
multi-task-learning
sequence-modeling
Miscellaneous
-
https://adamkarvonen.github.io/machine_learning/2024/03/20/chess-gpt-interventions.html: -
https://blog.research.google/2022/07/training-generalist-agents-with-multi.html: -
https://sites.google.com/view/multi-game-transformers:View HTML (22MB):
/doc/www/sites.google.com/2dbf2c3b6b6ec9ebae5f727ce473c1db01c6bbc9.html -
https://www.lesswrong.com/posts/D7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post -
https://www.lesswrong.com/posts/ukTLGe5CQq9w8FMne/inducing-unprompted-misalignment-in-llms
Link Bibliography
-
https://arxiv.org/abs/2308.09175#deepmind: “Diversifying AI: Towards Creative Chess With AlphaZero (AZdb)”, -
https://arxiv.org/abs/2209.12892: “g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, -
https://arxiv.org/abs/2208.10291: “Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, -
https://arxiv.org/abs/2206.13499: “Prompting Decision Transformer for Few-Shot Policy Generalization”, -
https://openreview.net/forum?id=0ZbPmmB61g#google: “Boosting Search Engines With Interactive Agents”, -
https://arxiv.org/abs/2205.14953: “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, -
https://arxiv.org/abs/2205.15241#google: “Multi-Game Decision Transformers”, -
https://arxiv.org/abs/2205.06175#deepmind: “Gato: A Generalist Agent”, -
https://arxiv.org/abs/2202.07415#deepmind: “NeuPL: Neural Population Learning”, -
https://trajectory-transformer.github.io/: “Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, -
https://sites.google.com/berkeley.edu/decision-transformer: “Decision Transformer: Reinforcement Learning via Sequence Modeling”, -
https://arxiv.org/abs/2104.11980: “Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, -
https://github.com/ricsonc/transformers-play-chess/blob/master/README.md: “Transformers Play Chess”, -
https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-game/: “A Very Unlikely Chess Game”,