Bridging the gap between AI and industrial controls

Our paper titled SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments has recently been accepted by NeurIPS 2022, Datasets and Benchmarks Track.

In this work, we built on top of our previous work, QuarticGYM with extensive benchmarks on online and offline, model-based and model-free reinforcement learning algorithms.

To our knowledge, this is the first paper that introduces a high-quality, standardized interface for controlling biochemical processes with AI-based, reinforcement learning alike algorithms.

We believe that it helps AI researchers explore applying advanced ai algorithms like deep reinforcement learning to control manufacturing processes, and it is one step closer to achieving autonomous control and industry 4.0 with Artificial Intelligence.

Please refer to the paper for more details and feel free to reach out to us for further discussion! 

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