From the series: Modeling, Simulation and Control
Sebastian Castro, MathWorks
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 MATLAB Central File Exchange.
For more information, you can access the following resources:
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .Select web site
You can also select a web site from the following list:
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.