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