@article{MAKHILLAJ202015620714,
    title = {Deep Reinforcement Learning Applied to Cart Pole Game},
    journal = {Agricultural Journal},
    volume = {15},
    number = {6},
    pages = {137-142},
    year = {2020},
    issn = {1816-9155},
    doi = {aj.2020.137.142},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9155&doi=aj.2020.137.142},
    author = {Abdallah and},
    keywords = {Deepmind deep learning,reinforcement learning,cart pole,Markov decision process,Bellman equation},
    abstract = {Building an agent to play games might be done
in several ways, like mini-max, Monte Carlo tree search,
deep learning or it could be a combination of two or three
technics or even more, like the popular chess computer
engine deep blue or alpha go. This document built an
agent that plays and balances cart pole game, the agent
used deep reinforcement learning-specifically Q-learning algorithm
to build neural network that greedy to maximize
the reward function and balance the pole for the longest
period at the end we got an agent that outperform the
random and human agent.}
    }