Unmanned Aerial Vehicles (UAVs or
drones) play a crucial role in surveillance missions,
especially where ground infrastructure is damaged or
inaccessible. Ensuring reliable and simultaneous coverage
of critical zones by UAVs, known as k-coverage,
remains a significant challenge. Traditional methods
require UAVs to cover an entire area which leads to high
energy consumption, this is problematic, especially in
environments where battery recharge or replacement
is difficult. To overcome these challenges, Only a set
of targets should be monitored instead of monitoring
the entire area. This paper proposes DQTCP (Deep
Q-learning-based Target Coverage Protocol), a new
deep reinforcement learning approach to continually
cover a maximum number of stationary targets. In
DQTCP, the UAV acts as an autonomous Deep QNetwork
(DQN) agent, with discrete actions and individualized
learning parameters balancing exploration
and exploitation. Through iterative training and environment
interaction, UAV adopts policies that optimize
the target coverage effectiveness. Simulations
show that DQTCP using based on the Reinforcement
learning theory, is very efficient in terms of coverage
performance and stability.
 

Target k-Coverage, UAVs, Reinforcement Learning, DQN.