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SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Mohan Zhang · Xiaozhou Wang · Benjamin Decardi-Nelson · Bo Song · An Zhang · Jinfeng Liu · Sile Tao · Jiayi Cheng · Xiaohong Liu · Dengdeng Yu · Matthew Poon · Animesh Garg

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #1021

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.

Author Information

Mohan Zhang (University of Toronto)
Xiaozhou Wang (Quartic AI)
Benjamin Decardi-Nelson (University of Alberta)
Bo Song
An Zhang (University of Alberta)
Jinfeng Liu
Sile Tao (Quartic.ai)
Jiayi Cheng
Xiaohong Liu (Shanghai Jiaotong University)
Dengdeng Yu (University of Texas at Arlington)
Matthew Poon
Animesh Garg (Georgia Institute of Technology)

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