4 accepted papers
Long-horizon robotic manipulation remains a critical challenge in robotics. Hierarchical reinforcement learning offers a promising solution, but often suffers from an imbalance dilemma: simplifying skill learning increases the complexity of planning, thereby expanding the solution space and computat…
Large-scale three dimensional vehicle aerodynamics prediction poses critical computational challenges in modern automotive design, where traditional CFD methods require prohibitive simulation times that conflict with rapid design iteration demands. While recent neural operator approaches show promis
Asynchronous stochastic gradient descent (ASGD) is widely adopted in distributed and federated learning. In this paper, we develop a sharp generalization analysis for ASGD by leveraging the concept of on-average model stability. For convex and smooth objectives, we establish stability and excess ris…
Recently, the real-world applications of reinforcement learning (RL) have seen the problem of taking actions in an out-of-distribution (OOD) state. However, most existing research is limited to take actions to narrow the visited training distribution and OOD, and does not consider the efficiency to…