Playing football game using AI agents

A lot of effort has been put into training AI agents to play games like chess, connect-4 etc where they excelled and taught us new ways to approach problems. Furtheron, recent progress in this field is accelerated by broadening horizons and taking on complex environments like GO. Football is also such a complex setting which requires the agent to learn intricate concepts like passing, shooting, dribbling etc and develop tactics to maximize chances of winning. And hence we take on the game of football using AI agents. We use the google reinforcement learning environment to train and evaluate our agents. We solve this problem of playing football by training two different agents namely Deep Q Networks and Light GBM, where Deep Q Network is a self-learning algorithm based on reinforcement learning and Light GBM is a supervised learning algorithm and dataset for this algorithm is extracted through kaggle.