论文标题
用于随机模型预测控制的异质贝叶斯优化
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
论文作者
论文摘要
模型预测控制（MPC）在涉及复杂物理系统控制的应用中已成功。这类控制器利用系统动力学的近似模型提供的信息来模拟控制动作的效果。 MPC方法还提供了一些超参数，这些参数可能通过要求与物理系统进行相互作用来需要相对昂贵的调整过程。因此，我们在随机MPC的背景下研究了微调的MPC方法，该方法由于控制器的动作的随机性而提出了额外的挑战。在这些情况下，性能结果存在噪声，在可能的超参数设置的整个域中并非均匀，但它以输入依赖性方式变化。为了解决这些问题，我们提出了一个贝叶斯优化框架，该框架解释了异质噪声，以调节控制问题中的超参数。基准连续控制任务和物理机器人的经验结果支持所提出的框架相对于基线的适用性，这不考虑异方差。
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided by an approximate model of the system's dynamics to simulate the effect of control actions. MPC methods also present a few hyper-parameters which may require a relatively expensive tuning process by demanding interactions with the physical system. Therefore, we investigate fine-tuning MPC methods in the context of stochastic MPC, which presents extra challenges due to the randomness of the controller's actions. In these scenarios, performance outcomes present noise, which is not homogeneous across the domain of possible hyper-parameter settings, but which varies in an input-dependent way. To address these issues, we propose a Bayesian optimisation framework that accounts for heteroscedastic noise to tune hyper-parameters in control problems. Empirical results on benchmark continuous control tasks and a physical robot support the proposed framework's suitability relative to baselines, which do not take heteroscedasticity into account.
