Reinforcement Learning

  Reinforcement Learning

Modern powertrains are evolving into electrified, interconnected, and software-intensive systems with many degrees of freedom. With development cycles getting shorter, this increase in system complexity leads to an exponentially rising amount of effort that has to be put into the development, validation, and calibration of software functions.

 

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Kevin Badalian

Research Associate

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+49 241 80 48181

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Lucas Koch

Research Associate

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+49 241 80 48105

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Current processes based on manually generated and calibrated software yield suboptimal solutions when faced with the rising number of variants and limited development capacities. Grasping the highly nonlinear relationships between different components, manipulated variables, parameters, and perturbations is not an easy task, not even for experts. Also, those solutions are generally not directly applicable to similar problems and require labor-intensive modifications to achieve that. This leaves potential efficiency improvements unexploited as development resources are limited and skilled personnel is scarce.

At the Teaching and Research Area Mechatronics in Mobile Propulsion (MMP), we use reinforcement learning (RL) as one of our main tools to reconcile the conflicting goals of efficiency improvement, reduction of emissions and other application-specific criteria. This machine learning method automatically derives an optimal strategy by trying to maximize a pre-defined reward function while interacting with an environment. Although its potential has already been demonstrated in numerous applications, RL has seen little use in the context of powertrain software and represents a novelty there.

Simulation methods utilizing X-in-the-loop (XiL) platforms are ideal for creating application-related training environments for the data-hungry algorithms in early development stages. The research at MMP ranges from the conceptual design of functions to the automation of the training process in various XiL simulators and the implementation in real-world applications.

 
 

Publications

Automated eco-driving in urban scenarios using deep reinforcement learning