9 Real-world decision-making with reinforcement learning
This chapter covers
- Fundamentals of formulating practical problems
- Reinforcement learning for applied real-world problems
- Challenges and limitations in classical optimization solutions
- How reinforcement learning can reinforce real-world decision making
God, grant me the serenity to accept the things I cannot change, courage to change the things I can, and wisdom to know the difference
Reinhold Niebuhr, Reformed theologian
It’s 8:00 a.m. on a Monday. In three different cities, three managers are facing a disaster. In Chicago, a retail manager is watching her pricing system fail in real time. The model was carefully built and fully optimized. But a product suddenly went viral on TikTok. The problem? The model was trained on five years of past data, before TikTok mattered. It keeps following its old rules and prices the product 30% too low. By the time someone notices, the company will have lost millions in revenue.
In Hamburg, a logistics manager sees his delivery plan fall apart. A rare storm has shut down a major port. But the routing software made its plan the night before and already sent out the trucks. Now his team is scrambling to reroute 400 vehicles by hand. What was optimal just hours ago has become the worst possible plan.