We consider the problem of designing scalable and portable controllers for unmanned aerial vehicles (UAVs) to reach time-varying formations as quickly as possible. This brief confirms that deep reinforcement learning can be used in a multi-agent fashion to drive UAVs to reach any formation while taking into account optimality and portability. We use a deep neural network to estimate how good a state is, so the agent can choose actions accordingly. The system is tested with different non-high-dimensional sensory inputs without any change in the neural network architecture, algorithm or hyperparameters, just with additional training.
@article{arxiv.1706.01384,
title = {Time-Varying Formation Controllers for Unmanned Aerial Vehicles Using Deep Reinforcement Learning},
author = {Ronny Conde and José Ramón Llata and Carlos Torre-Ferrero},
journal= {arXiv preprint arXiv:1706.01384},
year = {2017}
}