transformer in RL for decision-making
(we just upload partial references, and the left will be completed after our paper is published.)
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1.Transformer-based Offline RL
2.Transformer-based Online Reinforcement Learning
3.Trasnformer-based Hierarchical Reinforcement Learning
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1.Stability and Structure Optimization
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation[2021] E. Parisotto and R. Salakhutdinov[PDF]
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning [2022] K. Esslinger, R. Platt, and C. Amato[PDF][Github])
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation[2021] Nicklas Hansen, Hao Su, Xiaolong Wang[PDF][Github]
Transformer Based Reinforcement Learning For Games[2019] Uddeshya Upadhyay, Nikunj Shah, Sucheta Ravikanti, Mayanka Medhe [PDF]
Training Agents using Upside-Down Reinforcement Learning[2019] Rupesh Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, &Jürgen Schmidhuber [PDF]
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Offline Reinforcement Learning as One Big Sequence Modeling Problem[2021] Michael Janner, Qiyang Li, & Sergey Levine [PDF] [Github]
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems[2020] Sergey Levine, Aviral Kumar, George Tucker, & Justin Fu[PDF]
Decision Transformer: Reinforcement Learning via Sequence Modeling[2021] Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, & Igor Mordatch [PDF][Github1][Github2]
Bootstrapped Transformer for Offline Reinforcement Learning[2022] Kerong Wang, Hanye Zhao, Xufang Luo, Kan Ren, Weinan Zhang, & Dongsheng Li [PDF][Github]
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning[2022] Qinjie Lin, Han Liu, & Biswa Sengupta[PDF]
Human-level Atari 200x faster[2022] Steven Kapturowski, V'ictor Campos, Ray Jiang, Nemanja Raki'cevi'c, Hado van Hasselt, Charles Blundell, & Adri`a Puigdom`enech Badia [PDF]
On the Opportunities and Risks of Foundation Models[2021] Rishi Bommasani et al. [PDF]
Pretrained Transformers as Universal Computation Engines[2021] Kevin Lu, Aditya Grover, Pieter Abbeel, & Igor Mordatch[PDF][Github]
Online Decision Transformer[2022] Qinqing Zheng, Amy Zhang, & Aditya Grover[PDF]
Can Wikipedia Help Offline Reinforcement Learning?[2022] Machel Reid, Yutaro Yamada, & Shixiang Shane Gu[PDF][Github]
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge[2022] Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, & Anima Anandkumar[PDF][Github]
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Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks[2022] Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xiyun Li, Weinan Zhang, Ying Wen, Haifeng Zhang, Jun Wang, & Bo Xu [PDF][Github]
Gradient Surgery for Multi-Task Learning[2020] Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, & Chelsea Finn [PDF][Github]
Pretraining in Deep Reinforcement Learning: A Survey[2022] Zhihui Xie, Zichuan Lin, Junyou Li, Shuai Li, & Deheng Ye[PDF]
Hierarchical Reinforcement Learning: A Comprehensive Survey ACM Computing Surveys[2021] Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, & Chai Quek[PDF]
Hierarchical Decision Transformer[2022] Andr'e Correia, & Lu'is A. Alexandre[PDF]
Reinforcement Learning with Hierarchies of Machines[1997] Ronald Parr, & Stuart Russell[PDF]
Learning Multi-Level Hierarchies with Hindsight[2017] Andrew Levy, George Konidaris, Robert W. Platt, & Kate Saenko [PDF][Github]
An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective[2020] Yaodong Yang, & Jun Wang[PDF]
Multi-agent deep reinforcement learning: a survey[2021] Sven Gronauer, & Klaus Diepold[PDF]
The StarCraft Multi-Agent Challenge[2019] Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, & Shimon Whiteson[PDF][Github]
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments[2017] Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, & Igor Mordatch[PDF][Github]
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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning[2018] Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, & Shimon Whiteson[PDF]
The Surprising Effectiveness of MAPPO in Cooperative, Multi-Agent Games[2021] Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre M. Bayen, & Yi Wu[PDF] [Github]
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward[2018] Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, & Thore Graepel[PDF]
Multi-Agent Determinantal Q-Learning[2020] Yaodong Yang, Ying Wen, Liheng Chen, Jun Wang, Kun Shao, David Mguni, & Weinan Zhang[PDF][Github]
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft[2022] Muhammad Junaid Khan, Syed Hammad Ahmed, & Gita Sukthanka[PDF][Github]
Transform networks for cooperative multi-agent deep reinforcement learning[2022] Hongbin Wang, • Xiaodong Xie, & Lianke Zhou [PDF]
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers[2021] Siyi Hu, Fengda Zhu, Xiaojun Chang, & Xiaodan Liang[PDF][Github]
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem[2022] Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, & Yaodong Yang[PDF][Github]
Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing[2022] Yaodong Yang, Guangyong Chen, Weixun Wang, Xiaotian Hao, Jianye Hao, & Ann Heng [PDF][Github]
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning[2019] Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, & Sergey Levine [PDF][Github]
Meta-Learning in Neural Networks: A Survey[2020] Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, & Amos Storkey[PDF]
Meta-learning of Sequential Strategies[2019] Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Thomas M McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, & Shane Legg[PDF]
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning[2016] Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, & Pieter Abbeel [PDF]
Some Considerations on Learning to Explore via Meta-Reinforcement Learning[2018] Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, & Ilya Sutskever[PDF]
Transformers are Meta-Reinforcement Learners[2022] Luckeciano C. Melo [PDF][Github]
A model-based approach to meta-Reinforcement Learning: Transformers and tree search[2022] Brieuc Pinon, Jean-Charles Delvenne, & Rapha"el Jungers[PDF]
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents[2021] Jane X. Wang, Michael A. King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, & Matthew Botvinick [PDF][Github]
Contextual Transformer for Offline Meta Reinforcement Learning[2022] Runji Lin, Ye Li, Xidong Feng, Zhaowei Zhang, Xian Hong Wu Fung, Haifeng Zhang, Jun Wang, Yali Du, & Yaodong Yang[PDF]
Prompting Decision Transformer for Few-Shot Policy Generalization[2022] Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B Tenenbaum, & Chuang Gan[PDF][Github]
Offline Meta-Reinforcement Learning with Advantage Weighting[2020] Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, & Chelsea Finn[PDF][Github]
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scal[2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, & Neil Houlsby[PDF][Github]
How Crucial is Transformer in Decision Transformer?[2022] Max Siebenborn, Boris Belousov, Junning Huang, & Jan Peters[PDF]
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL[2022] Taku Yamagata, Ahmed Khalil, Raul Santos-Rodriguez (Intelligent System Laboratory, & University of Bristol)[PDF][Github]
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning[2021] Jinghuan Shang, Kumara Kahatapitiya, Xiang Li, & Michael S. Ryoo[PDF][Github]
Generalized Decision Transformer for Offline Hindsight Information Matching[2022] Hiroki Furuta, Yutaka Matsuo, & Shixiang Shane Gu[PDF][Github]
Settling the Variance of Multi-Agent Policy Gradients[2021] Jakub Grudzien Kuba, Muning Wen, Linghui Meng, Shangding Gu, Haifeng Zhang, David Mguni, Jun Wang, & Yaodong Yang[PDF]
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Imperfect Information Game in Multiplayer No-limit Texas Hold’em Based on Mean Approximation and Deep CFVnet[2021] Yuan Weilin, Hu Zhenzhen, Luo Junren, Xu Jiahui, Ji Xiang, Chen Shaofei, Zhang Wanpeng, & Chen Jing[PDF]
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Assistive Tele-op: Leveraging Transformers to Collect Robotic Task Demonstrations[2021] Henry M. Clever, Ankur Handa, Hammad Mazhar, Kevin Parker, Omer Shapira, Qian Wan, Yashraj Narang, Iretiayo Akinola, Maya Cakmak, & Dieter Fox[PDF]
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers[2021] Ruihan Yang, Minghao Zhang, Nicklas Hansen, Huazhe Xu, & Xiaolong Wang[PDF][Github]
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