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.”

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

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.