11 citations · 17 across the 14 of their papers we have counts for
4 papers · 1 filter
Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments
Haozhe Lei, Quanyan Zhu
In the area of learning-driven artificial intelligence advancement, the integration of machine learning (ML) into self-driving (SD) technology stands as an impressive engineering f…
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning
Tao Li, Haozhe Lei, Hao Guo +4
Millimeter-wave (mmWave) communication is a vital component of future generations of mobile networks, offering not only high data rates but also precise beams, making it ideal for…
Cognitive Level- Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving
Haozhe Lei, Quanyan Zhu
The potential market for modern self-driving cars is enormous, as they are developing remarkably rapidly. At the same time, however, accidents of pedestrian fatalities caused by au…
Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation
Tao Li, Haozhe Lei, Quanyan Zhu
Powered by deep representation learning, reinforcement learning (RL) provides an end-to-end learning framework capable of solving self-driving (SD) tasks without manual designs. Ho…