103 citations · 177 across the 7 of their papers we have counts for
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cs.LG2023★ 2 cited
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents
Jake Grigsby, Linxi Fan, Yuke Zhu
We introduce AMAGO, an in-context Reinforcement Learning (RL) agent that uses sequence models to tackle the challenges of generalization, long-term memory, and meta-learning. Recen…
cs.LG2022★ 53 cited
Pre-Trained Language Models for Interactive Decision-Making
Shuang Li, Xavier Puig, Chris Paxton +11
Language model (LM) pre-training is useful in many language processing tasks. But can pre-trained LMs be further leveraged for more general machine learning problems? We propose an…