When to ASK: Uncertainty-Gated Language Assistance for Reinforcement Learning
Reinforcement learning (RL) agents often struggle with out-of-distribution (OOD) scenarios, leading to high uncertainty and random behavior. While language models (LMs) contain valuable world knowledge, larger ones incur high computational costs, hindering real-time use, and exhibit limitations in autonomous planning. We introduce Adaptive Safety through Knowledge (ASK), which combines smaller LMs with trained RL policies to enhance OOD generalization without retraining. ASK employs Monte Carlo Dropout to assess uncertainty and queries the LM for action suggestions only when uncertainty exceeds a set threshold. This selective use preserves the efficiency of existing policies while leverag...
| Authors | Juarez Monteiro, Nathan Gavenski, Gianlucca Zuin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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