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OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome.

Assembly Line

Bridge the gap between Process Control and Reinforcement Learning with QuarticGym

📅 Date:

🔖 Topics: Autonomous Manufacturing

🏢 Organizations: Quartic AI, OpenAI

Modern process control algorithms are the key to the success of industrial automation. The increased efficiency and quality create value that benefits everyone from the producers to the consumers. The question then is, could we further improve it?

From AlphaGo to robot-arm control, deep reinforcement learning (DRL) tackled a variety of tasks that traditional control algorithms cannot solve. However, it requires a large and compactly sampled dataset or a lot of interactions with the environment to succeed. In many cases, we need to verify and test the reinforcement learning in a simulator before putting it into production. However, there are few simulations for industrial-level production processes that are publicly available. In order to pay back the research community and encourage future works on applying DRL to process control problems, we built and published a simulation playground with data for every interested researcher to play around with and benchmark their own controllers. The simulators are all written in the easy-to-use OpenAI Gym format. Each of the simulations also has a corresponding data sampler, a pre-sampled d4rl-style dataset to train offline controllers, and a set of preconfigured online and offline Deep Learning algorithms.

Read more at Quartic AI Blog