deep reinforcement learning for walking robots video -凯发k8网页登录
from the series:
sebastian castro demonstrates an example of controlling humanoid robot locomotion using deep reinforcement learning, specifically the deep deterministic policy gradient (ddpg) algorithm. the robot is simulated using simscape multibody™, while training the control policy is done using reinforcement learning toolbox™.
in this video, sebastian outlines the setup, training, and evaluation of reinforcement learning with simulink® models. first, he introduces how to choose states, actions, and a reward function for the reinforcement learning problem. then he describes the neural network structure and training algorithm parameters. finally, he shows some training results and discusses the benefits and drawbacks of reinforcement learning.
you can find the example models used in this video in the .
for more information, you can access the following resources:
您也可以从以下列表中选择网站:
如何获得最佳网站性能
选择中国网站(中文或英文)以获得最佳网站性能。其他 mathworks 国家/地区网站并未针对您所在位置的访问进行优化。
美洲
- (español)
- (english)
- (english)
欧洲
- (english)
- (english)
- (deutsch)
- (español)
- (english)
- (français)
- (english)
- (italiano)
- (english)
- (english)
- (english)
- (deutsch)
- (english)
- (english)
- switzerland
- (english)
亚太
- (english)
- (english)
- (english)
- 中国
- (日本語)
- (한국어)