Enforcing hard constraints with soft barriers: Safe reinforcement learning in unknown stochastic environments
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …
unknown and stochastic environment under hard constraints that require the system state …
[HTML][HTML] Trainify: A CEGAR-Driven Training and Verification Framework for Safe Deep Reinforcement Learning
P Jin, J Tian, D Zhi, X Wen, M Zhang - International Conference on …, 2022 - Springer
Abstract Deep Reinforcement Learning (DRL) has demonstrated its strength in developing
intelligent systems. These systems shall be formally guaranteed to be trustworthy when …
intelligent systems. These systems shall be formally guaranteed to be trustworthy when …
Efficient global robustness certification of neural networks via interleaving twin-network encoding
The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …
when being deployed in safety-critical systems, as it is important to analyze how sensitive …
Waving the double-edged sword: Building resilient cavs with edge and cloud computing
The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks,
and machine learning techniques have brought both opportunities and challenges for next …
and machine learning techniques have brought both opportunities and challenges for next …
Joint differentiable optimization and verification for certified reinforcement learning
Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …
Connectivity enhanced safe neural network planner for lane changing in mixed traffic
Connectivity technology has shown great potentials in improving the safety and efficiency of
transportation systems by providing information beyond the perception and prediction …
transportation systems by providing information beyond the perception and prediction …
Ablation study of how run time assurance impacts the training and performance of reinforcement learning agents
Reinforcement Learning (RL) has become an increasingly important research area as the
success of machine learning algorithms and methods grows. To combat the safety concerns …
success of machine learning algorithms and methods grows. To combat the safety concerns …
Reachability based online safety verification for high-density urban air mobility trajectory planning
View Video Presentation: https://doi. org/10.2514/6.2022-3542. vid This paper presents a
safe and scalable real-time trajectory planning framework for high-density Urban Air Mobility …
safe and scalable real-time trajectory planning framework for high-density Urban Air Mobility …
Interactive trajectory planner for mandatory lane changing in dense non-cooperative traffic
X Liu, J Chen, S Li, Y Zhang, H Yu, F Huang… - arXiv preprint arXiv …, 2023 - arxiv.org
When the traffic stream is extremely congested and surrounding vehicles are not
cooperative, the mandatory lane changing can be significantly difficult. In this work, we …
cooperative, the mandatory lane changing can be significantly difficult. In this work, we …
[HTML][HTML] Verification-guided programmatic controller synthesis
We present a verification-based learning framework VEL that synthesizes safe programmatic
controllers for environments with continuous state and action spaces. The key idea is the …
controllers for environments with continuous state and action spaces. The key idea is the …