Learning multi-agent pursuit of a moving target

Abstract

In this thesis we consider the task of catching a moving target with multiple pursuers, also known as the “Pursuit Game”, in which coordination among the pursuers is critical. Our testbed is inspired by the pursuit problem in video games, which require fast planning to guarantee fluid frame rates. We apply supervised machine learning methods to automatically derive efficient multi-agent pursuit strategies on rectangular grids. Learning is achieved by computing training data off-line and exploring the game tree on small problems. We also generalize the data to previously unseen and larger problems by learning robust pursuit policies, and run empirical comparisons between several sets of state features using a simple learning architecture. The empirical results show that 1) the application of learning across different maps can help improve game-play performance, especially on non-trivial maps against intelligent targets, and 2) simple heuristic works effectively on simple maps or less intelligent targets.