Reinforcement learning has the potential to solve tough decision-making problems in many applications, including industrial automation, autonomous driving, video game playing, and robotics. It is a type of machine learning in which a computer learns to perform a task through repeated interactions with a dynamic environment. This trial-and-error learning approach enables the computer to make a series of decisions without human intervention and without being explicitly programmed to perform the task. One famous example of reinforcement learning in action is AlphaGo, the first computer program to defeat a world champion at the game of Go.
In this webinar we will cover:
- Reinforcement Learning with MATLAB/Simulink
- What is Reinforcement Learning?
- Training Reinforcement Learning Agents
- Using DDPG Agents for Robotics Applications
- Building Environment Simulations with Simulink
- Comparing Reinforcement Learning to Optimization-based Controls Approaches
- Classical Controls versus Reinforcement Learning