标题： 不稳定潜在流形上的策略优化系统稳定化 显示英文标题

标题： System stabilization with policy optimization on unstable latent manifolds

摘要： 稳定性是在研究动力系统行为时的基本要求。 然而，通过强化学习来稳定动力系统具有挑战性，因为在不稳定性被触发且数据变得无意义之前，只能在短时间内收集到少量数据。 本文介绍了一种强化学习方法，该方法在不稳定动力系统的潜在流形上进行公式化，从而可以从少量数据样本中训练出稳定策略。 不稳定的流形是极小的，因为它们包含学习能够保证稳定的策略所必需的最低维动力学。 这与旨在近似所有——稳定和不稳定——系统动力学的一般潜在流形形成鲜明对比，因此这些流形维度更高，通常需要更多的数据。 实验表明，所提出的方法即使在仅有少量数据样本的情况下也能稳定复杂的物理系统，而其他在系统状态空间或一般潜在流形上操作的方法则会失败。 显示英文摘要

摘要： Stability is a basic requirement when studying the behavior of dynamical systems. However, stabilizing dynamical systems via reinforcement learning is challenging because only little data can be collected over short time horizons before instabilities are triggered and data become meaningless. This work introduces a reinforcement learning approach that is formulated over latent manifolds of unstable dynamics so that stabilizing policies can be trained from few data samples. The unstable manifolds are minimal in the sense that they contain the lowest dimensional dynamics that are necessary for learning policies that guarantee stabilization. This is in stark contrast to generic latent manifolds that aim to approximate all -- stable and unstable -- system dynamics and thus are higher dimensional and often require higher amounts of data. Experiments demonstrate that the proposed approach stabilizes even complex physical systems from few data samples for which other methods that operate either directly in the system state space or on generic latent manifolds fail.