Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price reduction and the increased capabilities of drones. The drones in the swarm need to cooperatively explore an unknown area, in order to identify and monitor interesting targets, while minimizing their movements. In this work, we propose a distributed Reinforcement Learning (RL) approach that scales to larger swarms without modifications. The proposed framework relies on the possibility for the UAVs to exchange some information through a communication channel, in order to achieve context-awareness and implicitly coordinate the swarm’s actions. Our experiments show that the proposed method can yield effective strategies, which are robust to communication channel impairments, and that can easily deal with non-uniform distributions of targets and obstacles. Moreover, when agents are trained in a specific scenario, they can adapt to a new one with minimal additional training. We also show that our approach achieves better performance compared to a computationally intensive look-ahead heuristic.

中文翻译：

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用于灵活高效的无人机群控制的分布式强化学习

在过去几年中，由于价格降低和无人机功能的增强，成群的无人机 (UAV) 在监控和偏远地区监视应用中的使用变得广泛。群中的无人机需要协同探索未知区域，以识别和监控感兴趣的目标，同时尽量减少它们的移动。在这项工作中，我们提出了一种分布式强化学习（RL）方法，无需修改即可扩展到更大的群体。所提出的框架依赖于无人机通过通信渠道交换一些信息的可能性，以实现上下文感知并隐式地协调群体的行动。我们的实验表明，所提出的方法可以产生有效的策略，这些策略对通信信道损伤具有鲁棒性，并且可以轻松处理目标和障碍物的非均匀分布。此外，当代理在特定场景中接受训练时，他们可以通过最少的额外训练来适应新场景。我们还表明，与计算密集型的前瞻启发式相比，我们的方法实现了更好的性能。