How to train realistic traffic agents for autonomous driving?
Analyzing Closed-loop Training Techniques for Realistic Traffic Agent Models in Autonomous Highway Driving Simulations
October 22, 2024
https://arxiv.org/pdf/2410.15987This research paper explores different ways to train AI agents to drive realistically in highway simulations. The study focuses on closed-loop training, where agents learn by directly interacting with a simulated environment and observing the consequences of their actions. This is contrasted with open-loop training, where agents only predict the next action without experiencing its result.
Key takeaways for LLM-based multi-agent systems:
- Closed-loop training (like differentiable simulation) is crucial for realistic multi-agent behavior. It helps avoid unrealistic scenarios that open-loop methods struggle with.
- Combining different training methods can be beneficial. For example, pairing data-driven imitation learning with reinforcement learning techniques, can improve performance while maintaining realism.
- Purely focusing on a single performance metric (like minimizing collisions) can harm overall realism. It's important to balance different objectives during training for best results.