  We introduce controlgym, a library of thirty-six 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
https://github.com/xiangyuan-zhang/controlgym.
