Monopoly Using Reinforcement Learning

This paper elucidates a machine learning model that learns to play a popular board game, namely monopoly. This model is trained and tested against various players, each with different strategies. The model applies a feedforward neural network with the concept of experience replay to learn to play the game. The model and this paper helps to reinforce the idea that there is no one strategy that will always win against any other strategy, while maintaining high win-rates.