How to train multi-vehicle navigation in unstructured environments faster?
Enhancing the Performance of Multi-Vehicle Navigation in Unstructured Environments using Hard Sample Mining
September 10, 2024
https://arxiv.org/pdf/2409.05119This paper presents a new method for training AI to control multiple vehicles in unstructured environments without relying on pre-defined traffic rules.
The key innovation is a technique called "hard sample mining" that focuses the AI's training on the most challenging scenarios where collisions are likely. This makes the training process more efficient and avoids the need for human labeling, which is difficult and subjective in such complex environments. This approach could be valuable for developing LLM-based multi-agent systems that can handle complex real-world situations.