“Can you please share how you created that model?” “I would love to see how one you combine machine-learning and simulation in practice”… I got many such messages after a recent post where I pondered about how simulation and machine-learning/AI can collaborate better.

Series overview

Hence, I decided to try a new format: in the next few weeks, I will publish a four-part video series exploring a conceptual AnyLogic model that applies reinforcement learning. It is build from scratch, i.e. doesn’t apply any external libraries and black-box approaches. This is probably the best way to learn about it :-)

In this first part, we will learn about reinforcement learning itslef, how it is used and why simulation is so critical.

Next, I will present the actual example model, dive deeper into the actual agents and their code and wrap up with the dynamic behavior of the model itself.

The video for part 1

You can view the first part below or directly on YouTube

Play the model yourself

You can always run and play with the model yourself by clicking below. If it doesn’t work, you can play it directly on the AnyLogic cloud here.

I would love to hear your feedback on this new format and what could be done differently. Would this be a good way to introduce more content in the future?

Hav fun

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