Today’s blog covers a recent publication by Amazon engineers. This paper describes how Reinforcement Learning (RL) is used to optimize Amazon’s inventory systems. The authors detail how a trained RL agent can optimize an inventory system to hold 12% less stock without any loss in revenue.
One of the complexities, and often a requirement, of an RL system is the ability to test and train the RL agent using a virtual representation of the world. This is often a limiting factor in using RL, if simulators are not available then typically building a custom simulator is required. Amazon was faced with this problem, but instead of building a simulator that models how the inventory system would be affected by certain changes, they developed a system that uses historical data to model the inventory state and dynamics which was then used as a learning environment for the RL agent. This technique can be adapted and applied to many different use cases allowing reinforcement learning to solve even more of the most complex challenges faced by enterprises.
The optimization of inventory aligns with our minds.ai Maestro scheduling solutions. Our customers use these solutions to jointly optimize their inventory as well as the production scheduling. The application of reinforcement learning for these use cases is a vast improvement over the typical dynamic programming solutions that are commonly used today.
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