"Playing Atari with Deep Reinforcement Learning," focuses on training deep convolutional neural networks to play Atari 2600 games using reinforcement learning. The authors use a novel approach called Deep Q-learning, which combines Q-learning with experience replay, a technique that allows the agent to learn from past experiences and improve its performance. This paper explores the ability of deep learning models to learn from raw visual inputs and overcome the challenges associated with reinforcement learning tasks, ultimately achieving performance that surpasses or approaches that of human experts on several Atari games.

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