\section{conclusion}
In this paper, we have presented 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. We have conducted extensive experiments to evaluate the performance of our method, comparing it with state-of-the-art techniques such as DQN, A3C, and PPO.

Our experimental results demonstrate that our DRL agent outperforms existing methods in terms of both average game score and training time. This superior performance can be attributed to the efficient feature extraction capabilities of the CNN and the improved learning process enabled by experience replay and target networks. Additionally, our method exhibits faster convergence and lower loss values during training, indicating its effectiveness in learning optimal policies for playing Atari games.

In conclusion, our work contributes to the field of artificial intelligence by developing a DRL agent capable of playing Atari games with improved performance and efficiency. By building upon existing research and incorporating novel techniques, our method has the potential to advance the understanding of DRL and its applications in various domains, ultimately paving the way for the development of more intelligent and autonomous systems in the future. Further research could explore the integration of additional techniques, such as environment modeling and experience transfer, to enhance the agent's generalization and sample efficiency across diverse Atari game environments.