2 citations · 3 across the 11 of their papers we have counts for
11 papers
Learning Quadruped Walking from Seconds of Demonstration
Ruipeng Zhang, Hongzhan Yu, Ya-Chien Chang +3
Quadruped locomotion provides a natural setting for understanding when model-free learning can outperform model-based control design, by exploiting data patterns to bypass the diff…
RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning
Yuexin Bian, Jie Feng, Tao Wang +3
On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies,…
When Maximum Entropy Misleads Policy Optimization
Ruipeng Zhang, Ya-Chien Chang, Sicun Gao
The Maximum Entropy Reinforcement Learning (MaxEnt RL) framework is a leading approach for achieving efficient learning and robust performance across many RL tasks. However, MaxEnt…
Improving Value Estimation Critically Enhances Vanilla Policy Gradient
Tao Wang, Ruipeng Zhang, Sicun Gao
Modern policy gradient algorithms, such as TRPO and PPO, outperform vanilla policy gradient in many RL tasks. Questioning the common belief that enforcing approximate trust regions…
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…
Fractal Landscapes in Policy Optimization
Tao Wang, Sylvia Herbert, Sicun Gao
Policy gradient lies at the core of deep reinforcement learning (RL) in continuous domains. Despite much success, it is often observed in practice that RL training with policy grad…