Optimizing machine behavior in supply chain and industrial operations has been constrained by the inability of traditional solvers to handle complexity. Multi-objectives, multi-agents and low latency all presented problems. Deep reinforcement learning has produced significant performance gains in problems ranging from machine scheduling, order sequencing, autonomous vehicle routing, and price prediction. In this talk, Chris Nicholson (Pathmind) will discuss how these solutions learned new behavior while using Ray and RLlib.
Audience Q&A follows the presentation.
Speakers
Chris Nicholson
Chris is the founder of Pathmind, an industrial AI company backed by Y Combinator, Ron Conway's SV Angel, Tencent and Translink Capital. Pathmind helps simulation modelers improve performance with deep reinforcement learning. Chris has worked in AI since 2014. He attended Deep Springs College, holds a degree in economics, and lives in the Bay Area.