In continual learning (CL), an AI agent (e.g., autonomous vehicles or
robotics) learns from non-stationary data streams under dynamic environments.
For the practical deployment of such applications, it is important to guarantee
robustness to unseen environments while maintaining past experiences. In this
paper, a novel CL framework is proposed to achieve robust generalization to
dynamic environments while retaining past knowledge. The considered CL agent
uses a capacity-limited memory to save previously observed environmental
information to mitigate forgetting issues. Then, data points are sampled from
the memory to estimate the distribution of risks over environmental change so
as to obtain predictors that are robust with unseen changes. The generalization
and memorization performance of the proposed framework are theoretically
analyzed. This analysis showcases the tradeoff between memorization and
generalization with the memory size. Experiments show that the proposed
algorithm outperforms memory-based CL baselines across all environments while
significantly improving the generalization performance on unseen target
environments.

提出了一种新颖的连续学习（CL）框架，通过使用有限容量的内存保存先前观察到的环境信息以减轻遗忘问题，并从内存中采样数据点来估计环境变化的风险分布，从而获得对未知变化具有鲁棒性的预测器。实验表明，该算法在所有环境中优于基于内存的 CL 基线，并显著提高了在未知目标环境中的泛化性能。