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.

翻译：在持续学习（CL）中，人工智能智能体（如自动驾驶车辆或机器人）需在动态环境下从非平稳数据流中学习。为确保此类应用的实际部署，在维持既往经验的同时保证对未知环境的鲁棒性至关重要。本文提出一种新型持续学习框架，旨在实现动态环境下的鲁棒泛化能力，同时保留历史知识。该持续学习智能体采用容量受限记忆单元存储先前观测到的环境信息以缓解遗忘问题，继而从记忆库中采样数据点以估计环境变化下的风险分布，从而获取对未知变化具有鲁棒性的预测器。本文对该框架的泛化与记忆性能进行了理论分析，揭示了记忆容量与泛化能力之间的权衡关系。实验表明，所提算法在所有环境下均优于基于记忆的持续学习基线方法，并在未知目标环境中显著提升了泛化性能。