In-Context Reinforcement Learning
GitHub - dunnolab/awesome-in-context-rl: Awesome In-Context RL: A curated list of In-Context Reinforcement Learning

      


    
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  Repository files navigationREADMEAwesome In-Context Reinforcement Learning
This is a collection of research papers for In-Context Reinforcement Learning (ICRL). The repository shall be regularly updated to track the frontiers.
Curated by dunnolab.

Please, feel free to PR new papers and resources you believe are relevant and awesome.
format:
- [title](paper link)
  - author1, author2, and author3...

Papers

In-context Reinforcement Learning with Algorithm Distillation

Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih


Supervised Pretraining Can Learn In-Context Reinforcement Learning

Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill


Emergence of In-Context Reinforcement Learning from Noise Distillation

Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin, Viacheslav Sinii, Sergey Kolesnikov


In-Context Reinforcement Learning for Variable Action Spaces

Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Sergey Kolesnikov


In-Context Reinforcement Learning Without Optimal Action Labels

Juncheng Dong, Moyang Guo, Ethan X Fang, Zhuoran Yang, Vahid Tarokh


XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning

Alexander Nikulin, Ilya Zisman, Alexey Zemtsov, Viacheslav Sinii, Vladislav Kurenkov, Sergey Kolesnikov


ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI

Ahmad Elawady, Gunjan Chhablani, Ram Ramrakhya, Karmesh Yadav, Dhruv Batra, Zsolt Kira, Andrew Szot


Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models

Can Demircan, Tankred Saanum, Akshay K. Jagadish, Marcel Binz, Eric Schulz


AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents

Jake Grigsby, Linxi Fan, Yuke Zhu


Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Licong Lin, Yu Bai, Song Mei


Structured State Space Models for In-Context Reinforcement Learning

Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, Feryal Behbahani


LLMs Are In-Context Reinforcement Learners

Giovanni Monea, Antoine Bosselut, Kianté Brantley, Yoav Artzi


EVOLvE: Evaluating and Optimizing LLMs For Exploration

Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen


In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought

Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun, Bo Yang


Retrieval-Augmented Decision Transformer: External Memory for In-context RL

Thomas Schmied, Fabian Paischer, Vihang Patil, Markus Hofmarcher, Razvan Pascanu, Sepp Hochreiter


Human-Timescale Adaptation in an Open-Ended Task Space

Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei Zhang


Generalization to New Sequential Decision Making Tasks with In-Context Learning

Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, Roberta Raileanu


Large Language Models can Implement Policy Iteration

Ethan Brooks, Logan Walls, Richard L. Lewis, Satinder Singh


Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration

Chentian Jiang, Nan Rosemary Ke, Hado van Hasselt


Towards General-Purpose In-Context Learning Agents

Louis Kirsch, James Harrison, C. Daniel Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber


Large Language Models as General Pattern Machines

Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, Andy Zeng


Cross-Episodic Curriculum for Transformer Agents

Lucy Xiaoyang Shi, Yunfan Jiang, Jake Grigsby, Linxi "Jim" Fan, Yuke Zhu


First-Explore, then Exploit: Meta-Learning Intelligent Exploration

Ben Norman, Jeff Clune


Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning

Tengye Xu, Zihao Li, Qinyuan Ren


Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning

Jonathan Cook, Chris Lu, Edward Hughes, Joel Z. Leibo, Jakob Foerster


Large Language Models As Evolution Strategies

Robert Tjarko Lange, Yingtao Tian, Yujin Tang


Can Large Language Models Explore In-Context?

Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins


SAD: State-Action Distillation for In-Context Reinforcement Learning under Random Policies

Weiqin Chen, Santiago Paternain


Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning

Subhojyoti Mukherjee, Josiah P. Hanna, Qiaomin Xie, Robert Nowak


Transformers Learn Temporal Difference Methods for In-Context Reinforcement Learning

Jiuqi Wang, Ethan Blaser, Hadi Daneshmand, Shangtong Zhang






      




    
  

          




    


  

    

    

    





    