AI智能体的记忆与泛化能力分析：持续学习者是否鲁棒？
Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?
AI总结：
本文提出一种新颖的持续学习框架，利用容量有限的记忆保存历史信息并采样估计风险分布，以在保留过去知识的同时实现对动态环境的鲁棒泛化，理论分析了记忆与泛化的权衡，实验证明其优于基线方法。
摘要：
在持续学习（CL）中，AI智能体（例如自动驾驶车辆或机器人）在动态环境下从非平稳数据流中学习。为了此类应用的实际部署，在维持过去经验的同时保证对未见环境的鲁棒性非常重要。本文提出了一种新颖的CL框架，旨在保留过去知识的同时实现对动态环境的鲁棒泛化。所考虑的CL智能体使用容量有限的记忆来保存先前观察到的环境信息，以缓解遗忘问题。然后，从记忆中采样数据点，以估计环境变化下的风险分布，从而获得对未见变化具有鲁棒性的预测器。本文从理论上分析了所提框架的泛化和记忆性能。该分析展示了记忆与泛化之间随记忆大小变化的权衡。实验表明，所提算法在所有环境中均优于基于记忆的CL基线，同时显著提高了在未见目标环境上的泛化性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
