Deep Reinforcement Learning: An Overview
It provides an overview for researchers interested in applying deep reinforcement learning, but it is incremental as it reviews existing work without new results.
This paper reviews the combination of deep learning and reinforcement learning to handle high-dimensional data, summarizing recent advances and commonly used architectures like autoencoders, CNNs, and RNNs.
In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.