Non-Linear Transformers Enable Cross-Domain Generalization in In-Context Reinforcement Learning

Researchers have proposed a novel approach to address the challenge of generalization in reinforcement learning (RL) by leveraging non-linear transformer architectures. Traditionally, RL models struggle to perform well on tasks beyond their training data, a issue often tackled through multi-task or meta-RL methods. This study introduces a framework where transformers act as functional operators, mapping context to task-specific functions via in-context learning without requiring explicit parameter updates. By establishing a theoretical connection between non-linear transformers and kernel-based temporal difference learning, the authors interpret the transformer as performing regression within a Reproducing Kernel Hilbert Space (RKHS). They demonstrate that value functions from diverse domains can share weights if they reside in the same RKHS. Experimental validation on multiple MetaWorld domains confirms this interpretation, showing successful convergence of the temporal-difference objective. This work provides significant insights into designing operators for stronger generalization in RL, suggesting that shared weight representations across domains are feasible under specific mathematical conditions, potentially advancing the efficiency and adaptability of AI agents in complex, varying environments.