This paper presents a deep learning model that successfully learns control policies for Atari 2600 games using raw pixel inputs and reinforcement learning. The model, a convolutional neural network trained with a variant of Q-learning, outperforms previous approaches on six out of seven games and surpasses human expert performance on three games. The authors address challenges such as sparse and delayed rewards, correlated data, and non-stationary distributions by using experience replay and stochastic gradient descent. The network was trained without any game-specific information or hand-designed features, demonstrating the potential of deep learning in reinforcement learning tasks.This paper presents a deep learning model that successfully learns control policies for Atari 2600 games using raw pixel inputs and reinforcement learning. The model, a convolutional neural network trained with a variant of Q-learning, outperforms previous approaches on six out of seven games and surpasses human expert performance on three games. The authors address challenges such as sparse and delayed rewards, correlated data, and non-stationary distributions by using experience replay and stochastic gradient descent. The network was trained without any game-specific information or hand-designed features, demonstrating the potential of deep learning in reinforcement learning tasks.