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

2017/06/05 by Ronny Conde, Conde, Ronny, J.R. Llata +3
Computer Science · #Adaptive Dynamic Programming Control #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.1706.01384

openalex publication_date 2017/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Playing Atari with Deep Reinforcement Learning
• ImageNet classification with deep convolutional neural networks

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