Abstract
In this work, we present an overview of a Deep Reinforcement
Learning method using Q-learning that recently brought
the state-of-the-art results in the Atari Emulator Environment.
This method is called DQN algorithm that combines successfully
a convolutional neural network with Q-learning. Successively
it was improved with Double Q-learning and other
valid techniques. Attracted by this hot research area we implemented
the DQN algorithm and run it in the CartPole environment,
a famous toy control challenge that allowed us to
deal with the main issues related to the implementation of
DQN. In this paper, we reported the results that we obtained,
our consideration of them and some personal thoughts about
the Deep Reinforcement Learning field.