mlsanityData to 14 Oct 2026

SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments

arXiv 2206.08851 ↗First posted 17 Jun 2022cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 69 references that are in the map. First posted on arXiv 17 Jun 2022.

Builds on

30 of 69 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2024Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systemscited by 5
  2. 2024CALE: Continuous Arcade Learning Environmentcited by 5
  3. 2023Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learningcited by 0
  4. 2026PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generationcited by 0