Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data

This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data. It uses a Deep Convolutional Autoencoder (DCAE) for learning compact feature spaces, in combination with recently-proposed Reinforcement Learning (RL) algorithms as Double DQN and Retrace...

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Methods used in the Paper


METHOD TYPE
Experience Replay
Replay Memory
Retrace
Value Function Estimation
AutoEncoder
Generative Models
Double Q-learning
Off-Policy TD Control
Q-Learning
Off-Policy TD Control
Double DQN
Q-Learning Networks
Dense Connections
Feedforward Networks
Convolution
Convolutions
DQN
Q-Learning Networks