We introduce controlgym, a library of thirty-six safety-critical industrial
control settings, and ten infinite-dimensional partial differential equation
(PDE)-based control problems. Integrated within the OpenAI Gym/Gymnasium (Gym)
framework, controlgym allows direct applications of standard reinforcement
learning (RL) algorithms like stable-baselines3. Our control environments
complement those in Gym with continuous, unbounded action and observation
spaces, motivated by real-world control applications. Moreover, the PDE control
environments uniquely allow the users to extend the state dimensionality of the
system to infinity while preserving the intrinsic dynamics. This feature is
crucial for evaluating the scalability of RL algorithms for control. This
project serves the learning for dynamics & control (L4DC) community, aiming to
explore key questions: the convergence of RL algorithms in learning control
policies; the stability and robustness issues of learning-based controllers;
and the scalability of RL algorithms to high- and potentially
infinite-dimensional systems. We open-source the controlgym project at
this https URL

我们介绍了 controlgym，这是一个包含 36 个安全关键的工业控制设置和 10 个基于无穷维偏微分方程的控制问题的库。我们将 controlgym 集成在 OpenAI Gym/Gymnasium (Gym) 框架中，允许直接应用标准的强化学习算法，如 stable-baselines3。此项目旨在为学习动态和控制（L4DC）社区提供服务，探索关键问题：学习控制策略的强化学习算法的收敛性；基于学习的控制器的稳定性和鲁棒性问题；以及强化学习算法在高维度和潜在无穷维度系统上的可扩展性。我们在 https URL 上开源了 controlgym 项目。