用时空核高斯过程安全追踪时变优化，无需提前检测变化
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
- 基于时空核的贝叶斯优化，自动适应时变系统
- 在合成数据上比SafeOpt更安全且更优
- 适合机器人、过程控制等时变安全优化场景
在机器人或过程控制等序列决策问题中，确保安全至关重要。由于底层系统复杂，当安全关键系统随时间变化时，寻找最优决策尤为困难。针对未知时变奖励函数和未知时变安全约束下的优化问题，我们提出TVSafeOpt算法，该算法基于带有时空核的贝叶斯优化，能够在不进行显式变化检测的情况下，安全追踪时变的安全区域。当优化问题变为平稳时，算法仍可提供最优性保证。在合成数据上的实验表明，TVSafeOpt在安全性和最优性方面均优于SafeOpt。在真实气体压缩机案例研究中进一步验证：面对未知的奖励函数和安全函数，TVSafeOpt能有效保障安全性。
原文摘要 · Abstract (English)
Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optimal decision challenging, especially when the safety-critical system is time-varying. Overcoming the problem of optimizing an unknown time-varying reward subject to unknown time-varying safety constraints, we propose TVSafeOpt, a new algorithm built on Bayesian optimization with a spatio-temporal kernel. The algorithm is capable of safely tracking a time-varying safe region without the need for explicit change detection. Optimality guarantees are also provided for the algorithm when the optimization problem becomes stationary. We show that TVSafeOpt compares favorably against SafeOpt on synthetic data, both regarding safety and optimality. Evaluation on a realistic case study with gas compressors confirms that TVSafeOpt ensures safety when solving time-varying optimization problems with unknown reward and safety functions.
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