4 papers
Safe Online Learning via Smooth Safety-Structured Policy Composition
Hongpeng Cao, Liqun Zhao, Yuliang Gu +3
Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics. Existing approaches typically rely on either stri…
Real-DRL: Teach and Learn in Reality
Yanbing Mao, Yihao Cai, Lui Sha
This paper introduces the Real-DRL framework for safety-critical autonomous systems, enabling runtime learning of a deep reinforcement learning (DRL) agent to develop safe and high…
Runtime Learning of Quadruped Robots in Wild Environments
Yihao Cai, Yanbing Mao, Lui Sha +2
This paper presents a runtime learning framework for quadruped robots, enabling them to learn and adapt safely in dynamic wild environments. The framework integrates sensing, navig…
Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning
Hongpeng Cao, Yanbing Mao, Lui Sha +1
Real-world accidents in learning-enabled CPS frequently occur in challenging corner cases. During the training of deep reinforcement learning (DRL) policy, the standard setup for t…