Continuous and Reinforcement Learning Methods for First-Person Shooter Games

Abstract

Machine learning is now widely studied as the
basis for artificial intelligence systems within computer games.
Most existing work focuses on methods for learning static
expert systems, typically emphasizing candidate selection. This
paper extends this work by exploring the use of continuous and
reinforcement learning techniques to develop fully-adaptive
game AI for first-person shooter bots. We begin by outlining a
framework for learning static control models for tanks within
the game BZFlag, then extend that framework using continuous
learning techniques that allow computer controlled tanks to adapt
to the game style of other players, extending overall playability by
thwarting attempts to infer the underlying AI. We further show
how reinforcement learning can be used to create bots that learn
how to play based solely through trial and error, providing game
engineers with a practical means to produce large numbers of
bots, each with individual intelligences and unique behaviours;
all from a single initial AI model.