Mitigating Distribution Shift in Reinforcement Learning through Ensemble-Based Uncertainty Quantification

Reinforcement Learning (RL) agents deployed in real-world environments often face the challenge of distribution shift, where the environment's dynamics change after training. This can lead to significant performance degradation. This paper investigates the use of ensemble-based uncertainty quantification to detect and mitigate the effects of distribution shift in RL. We propose a novel approach that leverages the disagreement between an ensemble of Q-functions as a proxy for distributional divergence, enabling proactive adaptation strategies. We demonstrate the effectiveness of our method in a simulated robotic navigation task under varying environmental conditions.