Controlling nonlinear dynamical systems using machine learning allows to not
only drive systems into simple behavior like periodicity but also to more
complex arbitrary dynamics. For this, it is crucial that a machine learning
system can be trained to reproduce the target dynamics sufficiently well. On
the example of forcing a chaotic parametrization of the Lorenz system into
intermittent dynamics, we show first that classical reservoir computing excels
at this task. In a next step, we compare those results based on different
amounts of training data to an alternative setup, where next-generation
reservoir computing is used instead. It turns out that while delivering
comparable performance for usual amounts of training data, next-generation RC
significantly outperforms in situations where only very limited data is
available. This opens even further practical control applications in real world
problems where data is restricted.

应用机器学习控制混沌参数 Lorenz 系统，研究表明下一代水库计算在数据有限的情况下可以显著优于传统水库计算，并在实际控制应用中具有进一步潜力。

使用机器学习控制动态系统达到复杂目标状态：下一代与经典的储层计算

Controlling dynamical systems to complex target states using machine  learning: next-generation vs. classical reservoir computing

Control of complex systems involves both system identification and controller
design. Deep neural networks have proven to be successful in many
identification tasks, however, from model-based control perspective, these
networks are difficult to work with because they are typically nonlinear and
nonconvex. Therefore many systems are still identified and controlled based on
simple linear models despite their poor representation capability. In this
paper we bridge the gap between model accuracy and control tractability faced
by neural networks, by explicitly constructing networks that are convex with
respect to their inputs. We show that these input convex networks can be
trained to obtain accurate models of complex physical systems. In particular,
we design input convex recurrent neural networks to capture temporal behavior
of dynamical systems. Then optimal controllers can be achieved via solving a
convex model predictive control problem. Experiment results demonstrate the
good potential of the proposed input convex neural network based approach in a
variety of control applications. In particular we show that in the MuJoCo
locomotion tasks, we could achieve over 10% higher performance using 5* less
time compared with state-of-the-art model-based reinforcement learning method;
and in the building HVAC control example, our method achieved up to 20% energy
reduction compared with classic linear models.

本文介绍使用凸神经网络实现模型精确度和控制可行性之间的平衡，通过设计凸性的循环神经网络来捕获动态系统的时态行为，并且可以通过解决凸问题来实现最优控制器。实验结果表明，该方法在控制应用中具有良好的潜力。

神经网络优化控制：凸逼近方法

Optimal Control Via Neural Networks: A Convex Approach

Control applications often feature tasks with similar, but not identical,
dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP),
a framework that parametrizes a family of related dynamical systems with a
low-dimensional set of latent factors, and introduce a semiparametric
regression approach for learning its structure from data. In the control
setting, we show that a learned HiP-MDP rapidly identifies the dynamics of a
new task instance, allowing an agent to flexibly adapt to task variations.

该论文介绍了一种名为隐藏参数马尔科夫决策过程 (HiP-MDP) 的框架，用于对控制应用中的不同动态系统进行参数化，并提出了一种半参数回归方法来学习其结构。在控制设置中，学习的 HiP-MDP 能够快速识别新任务实例的动态，使得智能体能够灵活适应任务的变化。