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:

• Reinforcement Learning Tech Talks
• Blog and Videos: Walking Robot Modeling and Simulation
• Paper: Continuous Control with Deep Reinforcement Learning
• Paper: Emergence of Locomotion Behaviours in Rich Environments