Probabilistic inference for fast learning in control

A novel framework is provided for very fast model-based reinforcement learning in continuous state and action spaces. It requires probabilistic models that explicitly characterize their levels of condence. Within the framework, exible, non-parametric models are used to describe the world based on previously collected experience. It demonstrates learning on the cart-pole problem in a setting where very limited prior knowledge about the task has been provided. Learning progressed rapidly, and a good policy found after only a small number of iterations.

三点弯曲试样在程序块谱、Rayleigh窄带谱和Wirsching宽带谱下的疲劳裂纹扩展计算

分别用分级应力法、等损伤应力法及均方根应力法对三点弯曲试件在程序块谱、窄带随机谱和宽带随机谱下的疲劳裂纹扩展进行了计算。以探讨均方根应力法是否适于三点弯曲试件在谱载下的疲劳裂纹扩展计算。