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Transformers for In-Context Reinforcement Learning
This paper **explores the theoretical underpinnings of using transformer networks for in-context reinforcement learning (ICRL)**. The authors propose a **general framework for supervised pretraining in meta-RL**, encompassing existing methods like Algorithm Distillation and Decision-Pretrained Transformers. They demonstrate that transformers can **efficiently approximate classical RL algorithms** such as LinUCB, Thompson sampling, and UCB-VI, achieving near-optimal performance in various settings. The research also provides **sample complexity guarantees** for the supervised pretraining approach and validates the theoretical findings through preliminary experiments. Overall, the work significantly contributes to understanding the capabilities of transformers in the domain of reinforcement learning.
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By Enoch H. KangThis paper **explores the theoretical underpinnings of using transformer networks for in-context reinforcement learning (ICRL)**. The authors propose a **general framework for supervised pretraining in meta-RL**, encompassing existing methods like Algorithm Distillation and Decision-Pretrained Transformers. They demonstrate that transformers can **efficiently approximate classical RL algorithms** such as LinUCB, Thompson sampling, and UCB-VI, achieving near-optimal performance in various settings. The research also provides **sample complexity guarantees** for the supervised pretraining approach and validates the theoretical findings through preliminary experiments. Overall, the work significantly contributes to understanding the capabilities of transformers in the domain of reinforcement learning.