CaRL: Learning Scalable Planning Policies with Simple Rewards
Bernhard Jaeger, Daniel Dauner, Jens Beißwenger, Simon Gerstenecker, Kashyap Chitta, Andreas Geiger
2025-04-30
Summary
This paper talks about CaRL, a new method for teaching AI how to make better decisions in tasks like self-driving cars by using a simpler way to give feedback during training.
What's the problem?
Training AI to handle complex tasks like driving usually needs complicated reward systems, which can make the process slow and hard to scale up as the training gets bigger.
What's the solution?
The researchers showed that by keeping the reward system simple, the AI can learn faster and perform better, especially when using larger groups of data and more computers at once.
Why it matters?
This matters because it means we can train smarter and safer self-driving cars and other robots more efficiently, making this technology more practical and accessible.
Abstract
Reinforcement learning with a simplified reward design achieves superior performance in autonomous driving tasks by scaling well with larger mini-batch sizes and distributed training.