Next-generation simulation software will incorporate deep reinforcement learning

Ben Lorica

The Data Exchange Podcast: Chris Nicholson on applications of reinforcement learning to business process simulation and optimization.


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In this episode of the Data Exchange I speak with Chris Nicholson, founder and CEO of Pathmind1, a startup applying deep reinforcement learning (DRL) to simulation problems.  In a recent post I highlighted two areas where companies can begin to add DRL to their suite of tools: personalization and recommendation engines, and simulation software. My interest in the interplay between DRL and simulation software began when I came across the work of Pathmind in this area.

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Our conversation focused on deep reinforcement learning and its applications:

  • We began with the basics: what is reinforcement learning and why should businesses pay attention to it?
  • We discussed enterprise applications of DRL, with particular emphasis in areas where Chris and Pathmind have been focused of late: Business Process Simulation and Optimization.
  • Pathmind have been early adopters of Ray and of RLlib, a popular open-source library for reinforcement learning built on top of Ray. I asked Chris why they chose to build on top of RLlib.

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[1] I am an advisor to Pathmind.

[Image: Direct Numerical Simulations and Robust Predictions of Cloud Cavitation Collapse by Jonas Sukys.]