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arXiv · 1706.01384

Time-Varying Formation Controllers for Unmanned Aerial Vehicles Using Deep Reinforcement Learning

Ronny Conde·José Ramón Llata·Carlos Torre-Ferrero
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Abstract

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.

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Ronny Conde, José Ramón Llata, Carlos Torre-Ferrero. 2017-06-05. Time-Varying Formation Controllers for Unmanned Aerial Vehicles Using Deep Reinforcement Learning. https://arxiv.org/abs/1706.01384

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