“Learning Humanoid Locomotion With Transformers”, Radosavovic Et Al 2023

“Learning Humanoid Locomotion with Transformers”⁠, Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, Koushil Sreenath2023-03-06 (knowledge distillation⁠, robotics; similar)

“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”⁠, Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian2022-08-22 (RL exploration; similar; bibliography)

“Goal-Conditioned Generators of Deep Policies”, Faccio Et Al 2022

“Goal-Conditioned Generators of Deep Policies”⁠, Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber2022-07-04 (meta-learning; similar)

“Prompting Decision Transformer for Few-Shot Policy Generalization”, Xu Et Al 2022

“Prompting Decision Transformer for Few-Shot Policy Generalization”⁠, Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, Chuang Gan2022-06-27 (meta-learning; similar; bibliography)

“Boosting Search Engines With Interactive Agents”, Ciaramita Et Al 2022

“Boosting Search Engines with Interactive Agents”⁠, Massimiliano Ciaramita, Leonard Adolphs, Michelle Chen Huebscher, Sascha Rothe, Christian Buck, Thomas Hofmann2022-06-04 (retrieval AI⁠, T5 Transformer⁠, RL exploration⁠, MuZero; similar; bibliography)

“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”⁠, Keiran Paster, Sheila McIlraith, Jimmy Ba2022-05-31 (similar)

“Multi-Game Decision Transformers”, Lee Et Al 2022

“Multi-Game Decision Transformers”⁠, Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer2022-05-30 (RL scaling; similar; bibliography)

“MAT: Multi-Agent Reinforcement Learning Is a Sequence Modeling Problem”, Wen Et Al 2022

“MAT: Multi-Agent Reinforcement Learning is a Sequence Modeling Problem”⁠, Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang2022-05-30 (MARL; similar; bibliography)

“Quark: Controllable Text Generation With Reinforced Unlearning”, Lu Et Al 2022

“Quark: Controllable Text Generation with Reinforced Unlearning”⁠, Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin Choi2022-05-26 (GPT⁠, preference learning; similar)

“Planning With Diffusion for Flexible Behavior Synthesis”, Janner Et Al 2022

“Planning with Diffusion for Flexible Behavior Synthesis”⁠, Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine2022-05-20 (diffusion model; backlinks; similar)

“Gato: A Generalist Agent”, Reed Et Al 2022

“Gato: A Generalist Agent”⁠, Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron2022-05-12 (meta-learning⁠, robotics⁠, RL scaling; similar; bibliography)

“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?”⁠, Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran2022-04-23 (CLIP⁠, robotics; similar)

“Learning Relative Return Policies With Upside-Down Reinforcement Learning”, Ashley Et Al 2022

“Learning Relative Return Policies With Upside-Down Reinforcement Learning”⁠, Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava2022-02-23 (similar)

“NeuPL: Neural Population Learning”, Liu Et Al 2022

“NeuPL: Neural Population Learning”⁠, Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel2022-02-15 (NN sparsity⁠, RL exploration⁠, meta-learning⁠, MARL; similar; bibliography)

“ODT: Online Decision Transformer”, Zheng Et Al 2022

“ODT: Online Decision Transformer”⁠, Qinqing Zheng, Amy Zhang, Aditya Grover2022-02-11 (RL exploration; similar)

“Can Wikipedia Help Offline Reinforcement Learning?”, Reid Et Al 2022

“Can Wikipedia Help Offline Reinforcement Learning?”⁠, Machel Reid, Yutaro Yamada, Shixiang Shane Gu2022-01-28 (ML dataset⁠, CLIP⁠, GPT⁠, RL scaling; similar)

“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”⁠, Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar2022-01-11 (meta-learning⁠, RL scaling; similar)

“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”⁠, Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xiyun Li, Weinan Zhang, Ying Wen, Haifeng Zhang2021-12-06 (MARL; backlinks; similar)

“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”⁠, Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli2021-10-20 (meta-learning; similar)

“Trajectory Transformer: Reinforcement Learning As One Big Sequence Modeling Problem”, Janner Et Al 2021

“Trajectory Transformer: Reinforcement Learning as One Big Sequence Modeling Problem”⁠, Michael Janner, Qiyang Colin Li, Sergey Levine2021-06-03 (RL exploration⁠, preference learning; backlinks; similar; bibliography)

“Decision Transformer: Reinforcement Learning via Sequence Modeling”, Chen Et Al 2021

“Decision Transformer: Reinforcement Learning via Sequence Modeling”⁠, Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas2021-06-02 (DALL·E⁠, inner monologue (AI); backlinks; similar; bibliography)

“Baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”, Alcorn & Nguyen2021

“baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents”⁠, Michael A. Alcorn, Anh Nguyen2021-04-24 (MARL; similar; bibliography)

“The Go Transformer: Natural Language Modeling for Game Play”, Ciolino Et Al 2020

“The Go Transformer: Natural Language Modeling for Game Play”⁠, Matthew Ciolino, David Noever, Josh Kalin2020-07-07 (backlinks; similar)

“Transformers Play Chess”, Cheng2020

“Transformers Play Chess”⁠, Ricson Cheng2020-01-10 (AI chess; backlinks; similar; bibliography)

“A Very Unlikely Chess Game”, Alexander2020

“A Very Unlikely Chess Game”⁠, Scott Alexander2020-01-06 (AI chess; backlinks; similar; bibliography)

“Training Agents Using Upside-Down Reinforcement Learning (UDRL)”, Srivastava Et Al 2019

“Training Agents using Upside-Down Reinforcement Learning (UDRL)”⁠, Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber2019-12-05 (similar)

“Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, Schmidhuber2019

“Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”⁠, Juergen Schmidhuber2019-12-05 (preference learning⁠, robotics; 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”