Machine Learning and Simulation:
Example and Downloads

While deep reinforcement learning is a new development in the world of artificial intelligence, and still mainly considered a research topic, simulation modeling has been in daily practical use for decades. It has a very mature community with a vast body of real-world examples.
 

Common practice in the simulation community is to take simulation models, run experiments (Optimization, Monte Carlo, parameter variation, etc.) and use the outputs to make better decisions about a model’s real-world counterpart.
With this approach, a human is needed to experiment with the simulation model and get information from it.

 

As mentioned earlier, recent developments in deep reinforcement learning have clearly demonstrated that learning agents (computer algorithms) are also very capable of extracting useful decisions (policies) from simulated systems. So, it makes sense to combine simulation modeling environments with machine learning, especially as interest moves away from gaming challenges and towards business-oriented objectives.

Reinforcement learning example model


To showcase the capabilities of a powerful general-purpose simulation tool as a training environment, we at AnyLogic, in partnership with Skymind, have developed a simple but illustrative example model based on the simulation of a traffic light-controlled intersection. A similar version of this model was demonstrated at the 2019 AnyLogic Conference in Austin, Texas, as part of a presentation on the practical application of deep reinforcement learning using AnyLogic.

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