Next-generation simulation software will incorporate deep reinforcement learning

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.

Ray Summit has been postponed until the Fall. In the meantime, enjoy an amazing series of virtual conferences beginning in mid May on the theme “Scalable machine learning, scalable Python, for everyone”. Go to anyscale.com/events for details.

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.]