This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is developed based on a general prediction-correction concept: the exact time-to-go under proportional navigation guidance with realistic aerodynamic characteristics is estimated by a deep neural network and a biased command to nullify the impact time error is developed by utilizing the emerging reinforcement learning techniques. The deep neural network is augmented into the reinforcement learning block to resolve the issue of sparse reward that has been observed in typical reinforcement learning formulation. Extensive numerical simulations are conducted to support the proposed algorithm.
Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions
Hong Kong University of Science and Technology（香港科技大学）；Mila - Québec AI Institute（魁北克AI研究院）；Université de Montréal（蒙特利尔大学）；Fudan University（复旦大学）；City University of Hong Kong（香港城市大学）
Beyond Token-Level Guidance: Inference-Time Alignment of Specialized LLMs via Cross-Family Representation Steering
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；College of Cryptology and Cyber Science, Nankai University（南开大学密码与网络科学学院）
Alignment of Diffusion Model and Flow Matching for Text-to-Image Generation
Department of Statistics, University of California, Los Angeles（加州大学洛杉矶分校统计学系）；Department of Industrial and Systems Engineering, University of Minneasota（明尼苏达大学工业与系统工程系）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）
