Vitesco Technologies Applies Deep Reinforcement Learning in Powertrain Control


“Reinforcement Learning Toolbox considerably reduced development time. The toolbox really helped in fast prototyping and generation of reinforcement learning agents.”

​-Vivek Venkobarao, Vitesco


 

 
 
 

 

Vitesco Technologies, which develops electrification technologies for all types of vehicles, has applied deep reinforcement learning for closed-loop powertrain control. Powertrain control systems must be able to handle a huge variety of environmental conditions. Global climate change and more stringent emission laws require faster development time, including accelerated prototyping.

Vitesco engineers used Simulink® to create a detailed model of the plant. Reinforcement Learning Toolbox™ enabled to quickly prototype, generate, and optimize reinforcement learning agents, considerably reducing development time.

 

Key Outcomes

  • Fast prototyping of reinforcement learning agents and reduced development time
  • Use of Simulink for state-of-the-art plant modeling
  • Quick start enabled through use of documentation and examples for reinforcement learning algorithms
  • Fast resolution to technical issues with dedicated calls with MathWorks experts

Products Used

  • Simulink
  • Deep Learning Toolbox
  • Reinforcement Learning Toolbox

[ebook] Explore reinforcement learning with MATLAB and Simulink

 

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