Off-Policy Reinforcement Learning with Gaussian Processes

An off-policy Bayesian nonparameteric approximate reinforcement learning framework,termed as GPQ,that employs a Gaussian processes(GP)model of the value(Q)function is presented in both the batch and online settings.Sufficient conditions on GP hyperparameter selection are established to guarantee convergence of off-policy GPQ in the batch setting,and theoretical and practical extensions are provided for the online case.Empirical results demonstrate GPQ has competitive learning speed in addition to its convergence guarantees and its ability to automatically choose its own bases locations.

空间编队飞行——分布式航天器系统开发了GPS的新功能

本文阐明:GPS测量方案和技术不断取得进展,业已开发出能对航天器编队飞行任务进行精确定位的GPS方案。从飞行高度高达30000km(超过GPS星座运行轨道的高度)的卫星上取得了初步的肯定结果。GPS将能充当一大类多个卫星编队飞行任务的导航系统,这将使未来空间科学研究发生革命。