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