Optimizing Ride Sharing
Karim discusses how to transform complex problems like ride sharing into actionable models using reinforcement learning. By leveraging real-world data from OpenStreetMap, he explains the importance of building a robust environment to optimize passenger pickups while minimizing delays. The conversation highlights the challenges of finding optimal solutions in NP hard logistics problems and the strategies employed to train these systems effectively.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Deep Reinforcement Learning for Logistics at Instadeep with Karim Beguir - #302
Related Questions
How does technology enable ride-sharing?
Can reinforcement learning be integrated into supply chains as discussed in the episode How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405?
Can reinforcement learning be integrated into supply chains as discussed in the episode How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405 and the clip Reinforcement Learning Insights?