- See Also
-
Links
- “Learning Humanoid Locomotion With Transformers”, Et Al 2023
- “Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, Et Al 2022
- “Goal-Conditioned Generators of Deep Policies”, Et Al 2022
- “Prompting Decision Transformer for Few-Shot Policy Generalization”, Et Al 2022
- “Boosting Search Engines With Interactive Agents”, Et Al 2022
- “You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments”, Et Al 2022
- “Multi-Game Decision Transformers”, Et Al 2022
- “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, 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
- “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
- “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
- “Training Agents Using Upside-Down Reinforcement Learning (UDRL)”, Et Al 2019
- “Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, 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”
- Miscellaneous
- Link Bibliography
See Also
Links
“Learning Humanoid Locomotion With Transformers”, Et Al 2023
“Learning Humanoid Locomotion with Transformers”, 2023-03-06 ( ; similar)
“Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, Et Al 2022
“Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, 2022-08-22 ( ; similar; bibliography)
“Goal-Conditioned Generators of Deep Policies”, Et Al 2022
“Goal-Conditioned Generators of Deep Policies”, 2022-07-04 ( ; similar)
“Prompting Decision Transformer for Few-Shot Policy Generalization”, Et Al 2022
“Prompting Decision Transformer for Few-Shot Policy Generalization”, 2022-06-27 ( ; similar; bibliography)
“Boosting Search Engines With Interactive Agents”, Et Al 2022
“Boosting Search Engines with Interactive Agents”, 2022-06-04 ( ; similar; bibliography)
“You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments”, Et Al 2022
“You Can’t Count on Luck: Why Decision Transformers Fail in Stochastic Environments”, 2022-05-31 (similar)
“Multi-Game Decision Transformers”, Et Al 2022
“Multi-Game Decision Transformers”, 2022-05-30 ( ; similar; bibliography)
“MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Et Al 2022
“MAT: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem”, 2022-05-30 ( ; similar; bibliography)
“Quark: Controllable Text Generation With Reinforced Unlearning”, Et Al 2022
“Quark: Controllable Text Generation with Reinforced Unlearning”, 2022-05-26 ( ; similar)
“Planning With Diffusion for Flexible Behavior Synthesis”, Et Al 2022
“Planning with Diffusion for Flexible Behavior Synthesis”, 2022-05-20 ( ; backlinks; similar)
“Gato: A Generalist Agent”, Et Al 2022
“Gato: A Generalist Agent”, 2022-05-12 ( ; similar; bibliography)
“Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?”, Et Al 2022
“Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?”, 2022-04-23 ( ; similar)
“Learning Relative Return Policies With Upside-Down Reinforcement Learning”, Et Al 2022
“Learning Relative Return Policies With Upside-Down Reinforcement Learning”, 2022-02-23 (similar)
“NeuPL: Neural Population Learning”, Et Al 2022
“NeuPL: Neural Population Learning”, 2022-02-15 ( ; similar; bibliography)
“ODT: Online Decision Transformer”, Et Al 2022
“ODT: Online Decision Transformer”, 2022-02-11 ( ; similar)
“Can Wikipedia Help Offline Reinforcement Learning?”, Et Al 2022
“Can Wikipedia Help Offline Reinforcement Learning?”, 2022-01-28 ( ; similar)
“In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, Et Al 2022
“In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, 2022-01-11 ( ; similar)
“Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, Et Al 2021
“Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks”, 2021-12-06 ( ; backlinks; similar)
“Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, Et Al 2021
“Shaking the foundations: delusions in sequence models for interaction and control”, 2021-10-20 ( ; similar)
“Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, Et Al 2021
“Trajectory Transformer: Reinforcement Learning as One Big Sequence Modeling Problem”, 2021-06-03 ( ; backlinks; similar; bibliography)
“Decision Transformer: Reinforcement Learning via Sequence Modeling”, Et Al 2021
“Decision Transformer: Reinforcement Learning via Sequence Modeling”, 2021-06-02 ( ; backlinks; similar; bibliography)
“Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, 2021
“baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, 2021-04-24 ( ; similar; bibliography)
“The Go Transformer: Natural Language Modeling for Game Play”, Et Al 2020
“The Go Transformer: Natural Language Modeling for Game Play”, 2020-07-07 (backlinks; similar)
“Transformers Play Chess”, 2020
“Transformers Play Chess”, 2020-01-10 ( ; backlinks; similar; bibliography)
“A Very Unlikely Chess Game”, 2020
“A Very Unlikely Chess Game”, 2020-01-06 ( ; backlinks; similar; bibliography)
“Training Agents Using Upside-Down Reinforcement Learning (UDRL)”, Et Al 2019
“Training Agents using Upside-Down Reinforcement Learning (UDRL)”, 2019-12-05 (similar)
“Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, 2019
“Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, 2019-12-05 ( ; similar)
“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”
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2208.10291
: “Trajectory Autoencoding Planner: Efficient Planning in a Compact Latent Action Space”, Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian: -
https://arxiv.org/abs/2206.13499
: “Prompting Decision Transformer for Few-Shot Policy Generalization”, Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, Chuang Gan: -
https://openreview.net/forum?id=0ZbPmmB61g#google
: “Boosting Search Engines With Interactive Agents”, : -
https://arxiv.org/abs/2205.15241#google
: “Multi-Game Decision Transformers”, : -
https://arxiv.org/abs/2205.14953
: “MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang: -
https://arxiv.org/abs/2205.06175#deepmind
: “Gato: A Generalist Agent”, : -
https://arxiv.org/abs/2202.07415#deepmind
: “NeuPL: Neural Population Learning”, Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel: -
https://trajectory-transformer.github.io/
: “Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, Michael Janner, Qiyang Colin Li, Sergey Levine: -
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”, Michael A. Alcorn, Anh Nguyen: -
https://github.com/ricsonc/transformers-play-chess/blob/master/README.md
: “Transformers Play Chess”, Ricson Cheng: -
https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-game/
: “A Very Unlikely Chess Game”, Scott Alexander: