Published Paper Details:

This work investigates the development and optimisation of reinforcement learning for predicting worst-case scenarios in launch vehicle simulations. Simulations take into account various environmental factors that can affect the launch, including wind conditions, temperature, atmospheric pressure and other parameters. Here we are trying to identify potential failure modes and anomalies that can occur during a rocket launch. Reinforcement learning models are trained using an objective function designed to accurately predict worst-case scenarios during a rocket launch. It also provides valuable insights into the factors contributing to worst-case scenarios, enabling targeted strategies for risk mitigation and system improvement. This approach aims to quantify the impact of individual parameters or their combinations on the predicted worst-case outcome. This paper demonstrates the potential of reinforcement learning in enhancing these launch vehicle simulations and fine-tuning algorithms to predict worst-case scenarios accurately. The developed models can inform decision-making and improve the overall resilience and efficiency of space missions by predicting and mitigating worst-case scenarios.