Deep RL Bootcamp Frontiers Lecture I: Recent Advances,

标签：obj   front   结束   Matter   学习   width   always   mit   eve   

high bias

if the robot has learnt something (no changes appear with iterations)

however, in the real world tasks, the task could change a little bit, then the robot will failed to generalize.

 no matter how well we train the robot in situations, there‘s always something that happens and messes up the system.

14 robot, sharing their own experience to each other

here the goal is to grasp anything. no task here

a few clips of our best trained neural network, picking up four objects that are visually kind of similar. They are all blue and roughly the same size, roughly rectangular.

 imitation learning

push the green tedy bear to the red spot

终于结束了，接下来需要

1，学习使用tensorflow和pytorch

2，对CNN、RNN、GAN动手实践，做项目

（大概需要20天）

3，学习基础的ML（大概需要4天）

4，学习raspberry pi和arduino（大概需要4天）

Deep RL Bootcamp Frontiers Lecture I: Recent Advances,

标签：obj   front   结束   Matter   学习   width   always   mit   eve   

原文地址：https://www.cnblogs.com/ecoflex/p/8991605.html