Human-level control through deep reinforcement learning

This paper demonstrates human-level performance in playing Atari 2600 games using deep reinforcement learning.

The paper presents a novel deep learning model, Deep Q-Network (DQN), which combines reinforcement learning with a deep convolutional neural network to achieve human-level control in playing Atari 2600 games. The model learns directly from raw pixels and rewards, without any prior knowledge of the game, and is able to outperform previous approaches as well as human players in several games. This work represents a significant advancement in the application of deep learning to complex control tasks.

• Introduction of the Deep Q-Network (DQN) model.
• Demonstration of human-level performance in Atari 2600 games.
• Learning directly from raw pixels and rewards without prior game knowledge.
• Outperforming previous approaches and human players in several games.

The model's performance is evaluated only on Atari 2600 games, which may not generalize to other types of tasks or environments. Future work could explore applying DQN to more diverse and complex environments, improving sample efficiency, and addressing stability issues during training.