Generation of safety-critical scenarios using multi-agent reinforcement learning

  • Subject:Reinforcement Learning, Scenario-based Testing, Motion Planning
  • Type:Masterarbeit
  • Date:ab 07 / 2023
  • Tutor:

    M. Sc. Joshua Ransiek

  • Zusatzfeld:

    Einsatzort am FZI Karlsruhe.

Generation of safety-critical scenarios using multi-agent reinforcement learning



The research area of machine learning is experiencing a renaissance due to hardware acceleration. One use case in the area of scenario-based testing is the identification and parameterization of relevant scenarios. Technical implementations of scenario-based testing approaches often rely on groups of these parameters, which are individually adapted in a time-consuming or labor-intensive manner. Reinforcement learning methods offer the possibility to learn such parameterizations through interaction even in complex environments. Based on state-of-the-art reinforcement learning algorithms, this thesis will investigate an approach and develop a method to generate safety-critical scenarios with multiple agents.


  • Familiarization with the theory of multi-agent reinforcement learning
  • Literature review on simulative generation of scenarios
  • Implementation of selected approaches in Python using PyTorch for RL algorithms
  • Investigation, comparison and processing of the results as well as documentation


  • Enthusiasm for the field of machine learning
  • Basic knowledge of Python or comparable programming languages
  • Independent thinking and working
  • Very good knowledge of German or English
  • Motivation, willingness to perform and commitment