TechCrunch article author here.

Our team at Pathmind has applied deep RL to multiple use cases in industrial control and supply chain management, notably various forms of scheduling, and MEIO, respectively.

You can see some demo simulations here:

https://pathmind.com/examples/

https://cloud.anylogic.com/model/15ee2fe9-c45c-425d-8b98-920...

https://cloud.anylogic.com/model/9c0725cc-7eb5-44ed-a2de-ff8...

https://cloud.anylogic.com/model/8769d942-43c4-4530-bc68-243...

https://cloud.anylogic.com/model/b7da42a0-734c-461f-9f68-382...

Yes, these show RL applied to simulations, and yes, real data and real physical plants are more complex than that.

But those physical systems are already controlled by optimizers (usually mathematical solvers like IBM Cplex or Gurobi), and deep RL happens to be an optimizer that can handle a few things better:

* data variability

* multiple objectives in complex scenarios

* multiple agents making simultaneous decisions in coordination

Solving the third problem gives rise to very interesting emergent behavior among teams of machines, which learn to behave in ways that are almost impossible for a hard-coded set of rules to specify. This is the source of many of the gains that RL produces.

I see a lot of preconceptions about RL in this thread that are partially true, but also solvable.

Yes, RL is data hungry. I acknowledged that in the TC piece. The problems with data are pretty much the same problems you get with ML in general. It's messy, partial, and usually not gathered with ML in mind. RL is no more impossible than other ML approaches, based on that argument.

A valid simulation of a physical system is a source of synthetic data that helps solve that problem.

Deep RL is already controlling machines on factory floors, and it will slowly help optimize larger and larger systems, from groups of assembly lines, to networks of plants.

In some cases it will serve as a "decisioning layer" (not my word) with HITL, and in others, it will drive more autonomous systems with low latency requirements.

Believe me or not, it's happening.

Without getting into too many details, the underlying algorithms are a flavor of policy gradient implemented with the open source projects Ray/RLlib (supported by AnyScale) and Gym (from OpenAI). They're great tools and I highly recommend them to anyone who wants to explore deep RL.

Fwiw, DeepMind says RL may be the path to AGI, and smarter people than me have placed their bets on that team, which has consistently surpassed expectations about what RL can do.

https://www.sciencedirect.com/science/article/pii/S000437022...

Microsoft is making a major push into this area using their Bonsai acquisition to power autonomous systems.

https://www.microsoft.com/en-us/ai/autonomous-systems

AWS, while not particularly focused on RL, is making a big push into operational technology (OT) as well.

The incentives driving their cloud business will spur them to transform the software that controls the factory floor, or at least prompt existing OT software makers like Siemens to move faster.

If you have any questions about how deep RL is being applied, my email is in my profile. Pls hit me up!