Robustness During Learning, Interaction and Adaptation for Autonomous Driving

Sammanfattning: In a sequential decision-making process, it is imperative to consider the potential risks of taking incorrect decisions throughout the whole process as all wrongdoings may not be possible to be remedied. This is particularly important when there are potentially catastrophic consequences. In this work, we develop robust decision-making processes, doing appropriate risk assessments where needed, to be able to plan to avoid unacceptable consequences. In contrast to traditional techniques for decision-making under uncertainty that aim to maximise performance in expectation, we choose to value other aspects out of the distribution of outcomes. For instance, in an application such as autonomous driving, the chance of causing an accident might be small yet fatal. A risk-averse decision-maker may choose to modify the risk criterion to only include consider e.g. the 25% worst-case outcomes to design a more robust decision-making process. We propose frameworks for quantifying uncertainty under the reinforcement learning framework and develop robust algorithms and theory that allow for risk-sensitive decision-making under uncertainty. Further, we study the interactions between multiple agents in autonomous systems and ways to deploy decision-making processes to novel scenarios by adaptation.