2 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
SEEV: Synthesis with Efficient Exact Verification for ReLU Neural Barrier Functions
Hongchao Zhang, Zhizhen Qin, Sicun Gao +1
Neural Control Barrier Functions (NCBFs) have shown significant promise in enforcing safety constraints on nonlinear autonomous systems. State-of-the-art exact approaches to verify…
Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
Milan Ganai, Sicun Gao, Sylvia Herbert
Recent literature has proposed approaches that learn control policies with high performance while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets ha…
Understanding the Difficulty of Solving Cauchy Problems with PINNs
Tao Wang, Bo Zhao, Sicun Gao +1
Physics-Informed Neural Networks (PINNs) have gained popularity in scientific computing in recent years. However, they often fail to achieve the same level of accuracy as classical…
Mollification Effects of Policy Gradient Methods
Tao Wang, Sylvia Herbert, Sicun Gao
Policy gradient methods have enabled deep reinforcement learning (RL) to approach challenging continuous control problems, even when the underlying systems involve highly nonlinear…
Extremum-Seeking Action Selection for Accelerating Policy Optimization
Ya-Chien Chang, Sicun Gao
Reinforcement learning for control over continuous spaces typically uses high-entropy stochastic policies, such as Gaussian distributions, for local exploration and estimating poli…
Sample-and-Bound for Non-Convex Optimization
Yaoguang Zhai, Zhizhen Qin, Sicun Gao
Standard approaches for global optimization of non-convex functions, such as branch-and-bound, maintain partition trees to systematically prune the domain. The tree size grows expo…