When to ASK: Uncertainty-Gated Language Assistance for Reinforcement Learning
This addresses the challenge of OOD generalization for RL agents, though it is incremental as it builds on existing methods with a novel integration mechanism.
The paper tackles the problem of reinforcement learning agents struggling with out-of-distribution scenarios by introducing ASK, which uses uncertainty gating to selectively query language models for assistance, achieving a reward of 0.95 in transfer tasks.
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 leveraging the language model's reasoning in uncertain situations. In experiments on the FrozenLake environment, ASK shows no improvement in-domain, but demonstrates robust navigation in transfer tasks, achieving a reward of 0.95. Our findings indicate that effective neuro-symbolic integration requires careful orchestration rather than simple combination, highlighting the need for sufficient model scale and effective hybridization mechanisms for successful OOD generalization.