TL;DR本文介绍了一种通过 Local Dynamics Model 和无模型策略学习相结合的方式有效地学习状态转移函数从而能够解决一步先见规划的复杂操纵任务的方法，并在模拟中展示了本方法的优越性。
Abstract
model-free policy learning has been shown to be capable of learning manipulation policies which can solve long-time horizon tasks using single-step manipulation primitives. However, training these policies is a t