HOW TO BUILD A REINFORCEMENT-LEARNING MODEL IN ANYLOGIC

“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 itself, 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 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?

Have fun :-)

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