用预训练神经策略实现快速安全主动学习，实时决策快上百倍
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies From Simulated Nonparametric Functions
- 用模拟非参数函数预训练神经策略，替代在线贝叶斯推断
- 部署时单次前向传播完成查询选择，速度提升数个数量级
- 适合对响应速度要求高的实时系统，如机器人控制
安全主动学习（AL）是一种在数据采集过程中遵守安全约束的序列学习方法。现有方法多依赖高斯过程（GPs）建模任务与安全约束，需反复更新并进行约束优化，计算开销大，难以支持实时决策。本文提出回归任务的摊销主动学习及安全主动学习框架，将昂贵的在线计算替换为预训练神经策略。受摊销贝叶斯实验设计启发，利用基于傅里叶特征的GP采样和可微的安全感知获取目标，在模拟非参数函数上预先训练策略。部署时，策略仅需一次前向传播即可选出信息量大且（可选）安全的查询点，避免了GP推断与优化。该方法在保持学习质量的同时实现数量级加速。框架模块化，去除安全组件后即适用于时间敏感的无约束主动学习任务。
原文摘要 · Abstract (English)
Safe active learning (AL) is a sequential scheme for learning unknown systems while respecting safety constraints during data acquisition. Existing methods often rely on Gaussian processes (GPs) to model the task and safety constraints, requiring repeated GP updates and constrained acquisition optimization--incurring significant computations which are challenging for real-time decision-making. We propose amortized AL for regression and amortized safe AL, replacing expensive online computations with a pretrained neural policy. Inspired by recent advances in amortized Bayesian experimental design, we leverage GPs as pretraining simulators. We train our policy prior to the AL deployment on simulated nonparametric functions, using Fourier feature-based GP sampling and a differentiable acquisition objective that is safety-aware in the safe AL setting. At deployment, our policy selects informative and (if desired) safe queries via a single forward pass, eliminating GP inference and acquisition optimization. This leads to magnitudes of speed improvements while preserving learning quality. Our framework is modular and, without the safety component, yields fast unconstrained AL for time-sensitive tasks.
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