A Comparative Evaluation of Reinforcement Learning Algorithms for Efficient Autonomous Spacecraft Landing

In the field of aerospace engineering, precision landing is crucial for ensuring the safety and success of spacecraft missions. This study compares the performance of three distinct algorithms designed for spacecraft landing scenarios, tested within a simulation environment developed in Unreal Engine. The algorithms are evaluated based on their accuracy, computational efficiency, and resource consumption. The results indicate that all three algorithms achieve successful landings with minimal resource utilization, though their performance varies under different conditions. This research provides insights into selecting the most suitable algorithm for spacecraft landing in resource-constrained environments.