SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments

Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Song Bo, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, Dengdeng Yu, Matthew Poon, Animesh Garg

TL;DR: A Collection of Manufacturing Simulation Environments, their advanced control baselines and reinforcement learning benchmarks.

Abstract: 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.

URL: https://github.com/smpl-env/smpl

Dataset Url:

The URL to the smpl package: https://github.com/smpl-env/smpl
The URL to the documentation of smpl: https://smpl-env.github.io/smpl-document/index.html
The URL to the smpl experiments: https://github.com/smpl-env/smpl-experiments

License:

The MIT License

Copyright (c) 2022 Quartic.ai (https://www.quartic.ai/)

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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Author Statement: Yes

Contribution Process Agreement: Yes

In Person Attendance: Yes

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/smpl-simulated-industrial-manufacturing-and/code)