Selectively querying language models based on uncertainty can improve RL agent robustness in novel situations without constant computational overhead—but successful integration requires careful design, not just combining the two systems.
This paper proposes ASK, a system that combines reinforcement learning agents with language models to handle out-of-distribution scenarios.