most citedAMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.RO2026

CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation

Letian Fu, Justin Yu, Karim El-Refai +13

"Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied ma…

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…