\begin{abstract}In this paper, we present a deep reinforcement learning (DRL) agent for playing Atari games using raw pixel inputs. Our proposed method combines a deep convolutional neural network (CNN) with a Q-learning algorithm, incorporating experience replay and target networks to improve the learning process. Through extensive experiments, we evaluate the performance of our method and compare it with state-of-the-art techniques such as DQN, A3C, and PPO. Our results demonstrate that our DRL agent outperforms existing methods in terms of both average game score and training time, indicating its effectiveness in learning optimal policies for playing Atari games. By building upon existing research and incorporating novel techniques, our work contributes to the field of artificial intelligence, advancing the understanding of DRL and its applications in various domains, and paving the way for the development of more intelligent and autonomous systems in the future.\end{abstract}