Improved Action Prediction through Multiple Model Processing of Player Trajectories

Action prediction in video games is the process of extracting useful information in order to predict the future actions of a player. Long-range dependencies and the dynamic nature of video games make it difficult for most algorithms to accurately predict the future actions of players. We propose a novel machine learning approach to improving future action prediction from video game trajectories. This method requires having first clustered player trajectories based on behaviour similarities. Our model consists of a set of LSTM based prediction modules each trained on a subset of data based upon a respective cluster. The effectiveness of our model is analysed on both a synthetic and natural dataset. We find that our future action prediction approach of leveraging multiple models trained on individual data subsets results in greater accuracy over a single model on a complete dataset.