In this session, we’ll explore industrial applications of reinforcement learning and compare the performance of an RL policy to traditional heuristics and optimizers. We have found that in certain use cases, RL can outperform all other approaches by more than 10%. We will cover the following topics:
1. A comparison of reinforcement learning versus heuristics and optimizers.
2. Bridging Ray with a simulation IDE such as AnyLogic to train a reinforcement learning policy.
3. A demo using a heating, ventilation, and air condition (HVAC) system.
At the conclusion of this session, you should be able to identify use cases suitable for RL and gain intuition on how reinforcement learning can be applied to industrial use cases.
Edward leads customer success at Pathmind. He has helped apply reinforcement learning to dozens of industrial use cases ranging from manufacturing, logistics, and warehousing problems. You may find examples of Edward's work at https://cloud.anylogic.com/models?public=true&orderType=BEST&selectedCategory=Selected%20Models&textSearch=pathmind.
Sahar is the lead simulation engineer at Pathmind where she works on automating integration of deep reinforcement learning with simulation models. She and her colleagues have delivered breakthrough performance gains in efficiency, throughput and cost to industrial operations and supply chains. Prior to Pathmind Sahar was with Simwell, a leading consultancy to Fortune 500 companies. She holds a Master’s degree in Industrial Engineering.