CS294-112深度增强学习课程（加州大学伯克利分校 2017）NO.4 Learning policies by imitating optimal controllers

标签：ems   optimize   ast   sch   gradient   city   learning   cto   some   

There are some problems: mismatch of model and reality; gradient explosion

so, the dynamics can be quite messy, and backpropogating can be quite problematic.

 sudden change in velocity and so on. schochastic system. gradient descent can be tough.

can we apply this trajectory optimization method to optimize policy?

GPS: guided policy search

in this case, o_t is from the camera and the joint velocity

CS294-112深度增强学习课程（加州大学伯克利分校 2017）NO.4 Learning policies by imitating optimal controllers

标签：ems   optimize   ast   sch   gradient   city   learning   cto   some   

原文地址：https://www.cnblogs.com/ecoflex/p/9078801.html