Game Machine and Algorithm towards Trends in Game States using Machine Learning and Deep Learning

Games are played worldwide by individuals with drastic and emerging needs for game machines and game-state learning algorithms. There are numerous sub-genres in the ever- expanding world of video games. Although there are many different types currently in use, many of them adhere to the same fundamental ideas. A hybrid approach of applying concepts from machine learning and deep learning is essential for the application of each of the games as their game states differ. In this paper, a hybrid approach of applying machine learning and deep learning algorithms towards the trends in the game stateis presented clearly and concisely. Furthermore, with the increasing needs of individuals involving game states, game environments many machine learning and deep learning algorithms have been developed. As the demands increase we felt that a hybrid approach involving machine learning and deep learning is equally important. Respectively, we have used a Markov Chain approach for the same. Likewise, we have presenteda detailed oriented view of the application of machine and deep learning in familiar games like cricket, poker, chess, and football.. In addition, we have presented the in-depth hybrid approach of the algorithm for game states in a game of chess.