Case study of Taxi Drop off problem (Reinforcement Learning)
In our increasingly digitized world, navigation apps have become indispensable tools. However, even with advanced GPS technology, these apps can sometimes lead us astray, suggesting inefficient routes or misguiding us entirely. Such shortcomings highlight the need for intelligent systems that can learn and adapt to dynamic urban environments.
This article explores the “Taxi Drop-Off Problem” as a case study to demonstrate how reinforcement learning can address these challenges. By training an agent (in this case, a virtual taxi) to navigate a simulated city, we can develop more efficient and reliable navigation systems.
