Deep Q-Network (DQN) algorithms combine both the reinforcement learning approach and the deep learning approach. DQN learns by itself, learning in an empirical way and without a rigid programming aimed at a particular objective, such as winning a game of chess.
DQN represents an application of Q-learning with the use of deep learning for the approximation of the evaluation function. The DQN was proposed by Mnih et al. through an article published in Nature on February 26, 2015. As a consequence, a lot of research institutes joined this field, because deep neural networks can empower reinforcement learning algorithms to directly deal with high-dimensional states.
The use of deep neural networks is due to the fact that researchers ...
