Self Learning Gaming Bot using CNN
Sanket Kine1, Mansi Londhe2, Akshada Milke3, Yadnyesh Modani4, Priya Pise5
1Sanket Kine*, Pursing Computer Engineering, Indira College of Engineering And Management, Maharashtra, India.
2Mansi Londhe, Currently Pursing Computer Engineering from Indira College of Engineering And Management, Maharashtra, India.
3Akshada Milk, Pursing Computer Engineering from Indira College of Engineering And Management, Maharashtra, India.
4Yadnyesh Modani, Computer Engineering student at Indira College of Engineering And Management, Maharashtra, India.
5Dr.Priya Pise, Professor at Indira College of Engineering, Maharashtra, India.
Manuscript received on January 22, 2020. | Revised Manuscript received on January 30, 2020. | Manuscript published on February 10, 2020. | PP: 2397-2401 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1833029420/2020©BEIESP | DOI: 10.35940/ijitee.D1833.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: In the present universe of current gaming condition bots are the intelligent agent that assumes a prominent job in the popularity of a game in the market. As these bots have gotten very unsurprising to the games. So here we are proposed an AI model for playing games with high level inputs using reinforcement learning. Algorithm works in the Atari Environment i.e. we are using 2D game. This model consists of the CNN (convolution neural network) for the inputs which is fully connected layers and find out the actions according to the inputs. In this learning -based approach, bots learned how to attack and ignore opponents so that bot can get maximum score. In this learning -based approach, bots learned how to attack and ignore opponents so that bot can get maximum score Then we tried the combine the input method which results maximum score of the bot in the environment for the better performance.
Keywords: Machine Learning, Artificial Intelligence, Genetic Algorithms, Artificial Neural Networks, Deep Q Learning , Double DQN Learning.
Scope of the Article: Machine Learning