Multi-agent Reinforcement Learning for Autonomous Vehicles in Wireless Sensor Networks

We develop a Deep Reinforcement Learning (DeepRL) based multi-agent algorithm to efficiently control

autonomous vehicles in the context of Wireless Sensor Networks (WSNs). In contrast to other applications, WSNs

have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the

quality of sensed data. Second, quality of service (QoS) which is used to measure the network’s performance. As

a use case, we consider wireless acoustic sensor networks; a group of speakers move inside a room and there

are microphones installed on vehicles for streaming the audio data. We formulate an appropriate Markov Decision

Process (MDP) and present, besides a centralized solution, a multi-agent Deep Q-learning solution to control the vehicles. We compare the proposed solutions to a naive heuristic and two different real-world implementations: microphones being hold or preinstalled. We show using simulations that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation and the proposed heuristic. Additionally, we provide theoretical analysis of the performance with respect to WSNs dynamics, such as speed, rooms dimensions and speaker’s talking time.