AINeutralarXiv – CS AI · 10h ago6/10
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One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning
Researchers propose a non-linear transformer architecture that enables reinforcement learning agents to generalize across different domains through in-context learning, establishing a theoretical connection between transformers and kernel-based temporal difference learning. By interpreting transformers as operators in Reproducing Kernel Hilbert Space, the work demonstrates that value functions from diverse domains can share a unified weight set, with MetaWorld experiments validating the approach.