The resilience of interdependent critical infrastructure systems (ICISs) is critical for the
functioning of society and the economy. ICISs such as power grids and telecommunication …

Solving optimal control problems serves as the basic demand of industrial control tasks.
Existing methods like model predictive control often suffer from heavy online computational …

Decision and control are core functionalities of high-level automated vehicles. Current
mainstream methods, such as functional decomposition and end-to-end reinforcement …

In the quest to advance human-centric natural language generation (NLG) systems,
ensuring alignment between NLG models and human preferences is crucial. For this …

In the area of autonomous driving, it typically brings great difficulty in solving the motion
planning problem since the vehicle model is nonlinear and the driving scenarios are …

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of
functions satisfying gradient dominance property with $1\le\alpha\le2 $ which holds in a …

We focus on learning the zero-constraint-violation safe policy in model-free reinforcement
learning (RL). Existing model-free RL studies mostly use the posterior penalty to penalize …

Entropy regularization is an efficient technique for encouraging exploration and preventing a
premature convergence of (vanilla) policy gradient methods in reinforcement learning (RL) …

In the paper, we design a novel Bregman gradient policy optimization framework for
reinforcement learning based on Bregman divergences and momentum techniques …

Intersections are quite challenging among various driving scenes wherein the interaction of
signal lights and distinct traffic actors poses great difficulty to learn a wise and robust driving …