Learning What They Pretend to Think: Adversarial ToM for Safety-Critical Driving Policies
In complex driving environments, autonomous agents must interact with diverse road users who exhibit heterogeneous and often unpredictable behaviors. Traditional reinforcement learning (RL) methods struggle to maintain robust performance in the presence of adversarial or deceptive intent. We propose...
Збережено в:
| Автори: | , , , |
|---|---|
| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
IEEE
2026-01-01
|
| Серія: | IEEE Access |
| Предмети: | |
| Онлайн доступ: | https://ieeexplore.ieee.org/document/11366238/ |
| Теги: |
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|