  The breakthrough of deep Q-Learning on different types of environments
revolutionized the algorithmic design of Reinforcement Learning to introduce
more stable and robust algorithms, to that end many extensions to deep
Q-Learning algorithm have been proposed to reduce the variance of the target
values and the overestimation phenomena. In this paper, we examine new
methodology to solve these issues, we propose using Dropout techniques on deep
Q-Learning algorithm as a way to reduce variance and overestimation. We also
present experiments conducted on benchmark environments, demonstrating the
effectiveness of our methodology in enhancing stability and reducing both
variance and overestimation in model performance.
