Research on reinforcement learning algorithms to play complex video games have brought forth controllers surpassing human performance. This paper explores the possibilities of applying these techniques to the sequencing and spacing of aircraft. Two experiments are performed. First a single aircraft must learn to fly a 4D trajectory using only heading commands. To train an agent Duelling Deep Q-Networks has been applied to learn a successful policy, however, learning is unstable and does not provide a suitable basis for extending this to a multi-agent setting. Second, a multi-agent experiment is performed where aircraft have to sequence and space themselves for landing without a 4D constraint. A Bidirectional Communication Net has been trained using Deep Deterministic Policy Gradients first on a single traffic scenario and then on multiple traffic scenarios. Emerging strategies have been seen in the single scenario training e.g. a holding, but no optimal policy was found. Training on multiple traffic scenarios showed no coordination efforts between the aircraft. Further analysis showed the importance of a proper reward function and exploration strategies which were likely the cause of not finding an optimal policy for a multi-agent setting.

Mountain ranges all over Earth have long been tourist attractions for their monumental size, beautiful nature, clean air and possible leisure activities. Though attractive, mountain activities may form a threat to human safety. Search and rescue (SAR) teams are constantly stand-by and often have multiple rescue sorties per day. These missions are slow and dangerous for the involved personnel. In extremely rare cases rescue missions use helicopters for search by pilot eyesight. These mission types on average cost e3, 300 per hour, are dangerous for personnel, have a small endurance and have a large downtime. There is a clear need to improve effectiveness and safety of these rescue missions...