While data-driven methods aim to build models for trajectory planning and conflicts resolution, incorporating stakeholders’ interests and preferences, the multi-agent reinforcement learning (MARL) approach aims to address complexity phenomena due to traffic and resolve conflicts between multiple trajectories, simultaneously. Towards that goal we aim to formulate the problem as a Markov Decision Making Process, i.e. MDP, and apply multi-agent reinforcement learning (MARL) methods to resolve it.

Reinforcement Learning, and particularly Q-learning has been studied in the context of predicting trajectories, exploiting historical data about trajectories, enhanced with aircraft intent information . This is a recently-proposed approach whose potential and limitations have been explored by the DART project .

datAcron project is funded by the European Union’s Horizon 2020 Programme under grant agreement No. 687591.

datAcron is a research and innovation collaborative project targeting at introducing novel methods to detect threats and abnormal activity of very large numbers of moving entities in large geographic areas.