Playing Atari with Deep Reinforcement Learning

A deep learning model using Q-learning successfully learns control policies from raw pixels and outperforms previous methods and human experts in Atari 2600 games.

This paper introduces a deep learning model that leverages a convolutional neural network trained with a variant of Q-learning to learn control policies directly from high-dimensional sensory input, specifically raw pixels. This approach was tested on seven Atari 2600 games within the Arcade Learning Environment. The results demonstrate that the model outperforms all previous methods on six of the games and even surpasses human expert performance on three of the games, without requiring any adjustments to the architecture or learning algorithm for different games.

• Introduction of a deep learning model that learns control policies from raw pixel input using reinforcement learning.
• Application of convolutional neural networks in conjunction with Q-learning to the domain of video games.
• Demonstration of the model's superior performance over previous approaches in six Atari 2600 games.
• Achievement of human expert-level performance in three Atari 2600 games.

Potential limitations include the need for significant computational resources for training deep learning models and the challenge of generalizing this approach to more complex or varied environments beyond the specific set of Atari 2600 games. Future work may explore optimization of the architecture and learning algorithm for broader applications and more efficient training processes.