A Distributed Reinforcement Learning Strategy to Maximize Coverage in a Hybrid Heterogeneous Sensor Network
Hesam Mosalli, Amir G. Aghdam · 2024
This paper introduces an efficient distributed deployment strategy for a network of mobile and stationary sensors with nonidentical sensing and communication radii. A collaborative distributed multi-agent deep reinforcement learning method is proposed to find the best moving direction and step size for each sensor, considering the coverage priority. The gradient of the local coverage function is used to generate a fast-converging solution as well as a learning-inspired arbitrary input to enable the network to avoid the local optima. The sensors use their partial observation of the network and field to iteratively relocate themselves to explore the field and learn the optimal policy to increase their local coverage. The efficiency of the proposed strategy in different scenarios is demonstrated by simulations.