论文标题
SMPL：模拟工业制造和过程控制学习环境
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
论文作者
论文摘要
传统的生物学和制药工厂由人类工人或预定义的阈值控制。现代化的工厂具有高级过程控制算法，例如模型预测控制（MPC）。但是，几乎没有探索深入的加固学习来控制制造厂。原因之一是缺乏高保真模拟和基准测试的标准API。为了弥合这一差距，我们开发了一个易于使用的库，其中包括五个高保真模拟环境：BeerfMtenv，Reactorenv，Atropineenv，Pensimenv和Mabenv，涵盖了广泛的制造过程。我们在已发布的动态模型上构建这些环境。此外，我们在线和离线基准基准，基于模型的和无模型的增强学习算法，用于比较后续研究。
Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds. Modernized factories have advanced process control algorithms such as model predictive control (MPC). However, there is little exploration of applying deep reinforcement learning to control manufacturing plants. One of the reasons is the lack of high fidelity simulations and standard APIs for benchmarking. To bridge this gap, we develop an easy-to-use library that includes five high-fidelity simulation environments: BeerFMTEnv, ReactorEnv, AtropineEnv, PenSimEnv and mAbEnv, which cover a wide range of manufacturing processes. We build these environments on published dynamics models. Furthermore, we benchmark online and offline, model-based and model-free reinforcement learning algorithms for comparisons of follow-up research.
