A Solution to Cartpole using Neural Networks and Tensorflow
Nishad Sandilya1, P Vinoth Kumar2
1Nishad Sandilya, Dept. of CSE, SRM University, Chennai, India.
2P Vinoth Kumar, Asst. Professor (Sr.G), Dept. of CSE, SRM University, Chennai, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 920-924 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7519118419/2019©BEIESP | DOI: 10.35940/ijrte.D7519.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Machine learning is not quite a new topic for discussion these days. A lot of enthusiasts excel in this field. The problem just lies with the beginners who lack just the right amount of intuition in to step ahead in this field. This paper is all about finding a simple enough solution to this issue through an example problem Cart-Pole an Open AI Gym’s classic Machine Learning algorithm benchmarking tool. The contents here will provide a perception to Machine Learning and will help beginners get familiar with the field quite a lot. Machine Learning techniques like Regression which further includes Linear and Logistic Regression, forming the basics of Neural Networks using familiar terms from Logistic regression would be mentioned here. Along with using TensorFlow, a Google’s project initiative which is widely used today for computational efficiency would be all of the techniques used here to solve the trivial game Cart-Pole.
Keywords: Artificial Intelligence, Cart-Pole using simple code, Deployment with TensorFlow, Machine Learning, Neural Networks, Python, TensorFlow
Scope of the Article: Artificial Intelligence and Machine Learning.