• Q-learning
• hole recovery
• WSN
• wireless sensor network
• sensor node
• reinforcement learning
• coverage
• coverage maximization

In this work, the implementation of a distributed algorithm based on Q-learning for hole recovery in Wireless Sensor Networks is described. Initially, a detailed analysis of the state-of-the-art for Hole Detection and Recovery has been carried out, focusing on Reinforcement Learning approaches. Then, a simulation environment for multi-agent reinforcement learning hole recovery algorithms has been implemented in Matlab. Subsequently, a state-of-the-art Q-learning based algorithm has been developed in the simulation environment, adding also a decaying rate for the exploration rate. Finally, Differential Evolution has been used for Hyperparameters Optimization.