M. Sc. Joshua Ransiek
- ESS/Scientific Staff
- Group: Prof. Sax
- Phone: +49 721 9654 174
- Ransiek ∂does-not-exist.fzi de
- www.fzi.de/team/joshua-ransiek/
Forschungszentrum Informatik (FZI)
Haid- und Neu-Str. 10 - 14
76131 Karlsruhe
Software Testing with Reinforcement Learning
Software verification and validation is a critical component of modern development processes. With the increasing functionality of modern software systems, the demand for efficient, scalable, and automated testing methods is growing. Reinforcement Learning (RL), with its approaches based on agent-environment interaction, offers a promising framework for automating software testing. RL is particularly suited for identifying complex failure scenarios triggered by specific and non-trivial sequences of states and actions. By iteratively adapting test strategies based on thier reward function, RL agents can dynamically generate test scenarios that uncover previously undetected bugs and anomalies. At ITIV&FZI, research is conducted on the development of novel RL methods that aim at optimizing the software testing process in terms of both efficiency and coverage of potential defect states, as well as facilitating the discovery of hard-to-detect anomalies.
Intelligent and Adaptive Traffic Simulation
The validation of automated driving functions poses a central challenge due to the discrepancy between simulations and real-world traffic situations. Previous approaches that rely on pre-recorded trajectories of traffic participants are limited, as these agents are unable to adequately respond to the behavior of automated vehicles. This leads to unrealistic simulation results that are insufficient for reliable evaluation of driving functions. Learning-based methods, however, offer new potential for developing realistic and reactive traffic agents that dynamically react to the behavior of automated vehicles and specifically generate critical traffic scenarios. This allows a targeted investigation of the behavior of automated vehicles in challenging situations and contributes to the improvement of their robustness. At ITIV&FZI, novel methods are investigated to efficiently implement these approaches and to significantly improve the validation of automated vehicles through realistic and adaptive simulation environments.