19 citations · 74 across the 18 of their papers we have counts for
5 papers · 1 filter
CAT: Closed-loop Adversarial Training for Safe End-to-End Driving
Linrui Zhang, Zhenghao Peng, Quanyi Li +1
Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a…
Guarded Policy Optimization with Imperfect Online Demonstrations
Zhenghai Xue, Zhenghao Peng, Quanyi Li +2
The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrati…
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Quanyi Li, Zhenghao Peng, Bolei Zhou
Human intervention is an effective way to inject human knowledge into the training loop of reinforcement learning, which can bring fast learning and ensured training safety. Given…
Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization
Zhenghao Peng, Quanyi Li, Ka Ming Hui +2
Self-Driven Particles (SDP) describe a category of multi-agent systems common in everyday life, such as flocking birds and traffic flows. In a SDP system, each agent pursues its ow…
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
Quanyi Li, Zhenghao Peng, Lan Feng +3
Driving safely requires multiple capabilities from human and intelligent agents, such as the generalizability to unseen environments, the safety awareness of the surrounding traffi…