Reinforcement Learning using Convolutional Neural Network for Game Prediction
Suvain Goyal1, Vaibhav Somani2, S.Sharanya3

1Suvain Goyal, Dept. of CSE SRM KTR Chennai, India.
2Vaibhav Somani, Dept. of CSE SRM KTR Chennai, India.
3Mrs S.Sharanya, Dept. of CSE SRM KTR Chennai, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 20, 2020. | Manuscript published on June 10, 2020. | PP: 425-430 | Volume-9 Issue-8, June 2020. | Retrieval Number: G5698059720/2020©BEIESP | DOI: 10.35940/ijitee.G5698.069820
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Abstract: The paper presents a Deep learning model for playing computer games with elevated level information utilizing Reinforcement learning learning. The games are activity restricted (like snakes, catcher, air-bandit and so on.). The implementation is progressive in three parts. The first part deals with a simple neural network, the second one with Deep Q network and further to increase the accuracy and speed of the algorithm, the third part consists of a model consisting of convolution neural network for image processing and giving outputs from the fully connected layers so as to estimate the probability of an action being taken based on information extracted from inputs where we apply Q-learning to determine the best possible move. The results are further analysed and compared to provide an overview of the improvements in each methods.
Keywords: Deep Q Network, Convolutional Neural Networks , Q-Learning.
Scope of the Article: Deep Learning