Authors: Bastian Birkeneder
Deep Q-Networks first introduced a combination of Reinforcement Learning and Deep Neural Networks at a large scale. These Networks are capable of learning their interactions within an environment in a self-sufficient manor for a wide range of applications. Over the following years, several extensions and improvements have been developed for Deep Q-Networks. In the following paper, we present the most notable developments for Deep Q-Networks, since the initial proposed algorithm in 2013.
Comments: 5 Pages.
[v1] 2019-03-10 19:47:08
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