Ms. Pac-Man is a challenging video game
in which multiple modes of behavior are
required to succeed: Ms. Pac-Man must
escape ghosts when they are threats,
and catch them when they are edible, in
addition to eating all pills in each level.
Past approaches to learning behavior in
Ms. Pac-Man have treated the game as a
single task to be learned using monolithic
policy representations.
In contrast, this paper uses
a framework called Modular Multiobjective NEAT to
evolve modular neural networks.
Each module defines
a separate policy;
evolution discovers
these policies and when
to use them.
The number of modules can be fixed or
learned using a new
version of a genetic operator, called
Module Mutation, which duplicates an existing
module that can then evolve to take on a distinct
behavioral identity. Both the fixed modular
networks and Module Mutation networks outperform
traditional monolithic networks. More interestingly,
the best modular networks dedicate modules to critical
behaviors that
do not follow the customary
division of the game into
chasing edible and escaping threatening ghosts.
[ Winner of the GECCO-2014 Best Paper Award in Digital Entertainment and Arts ]
[ An
expanded version of this article appears in TCIAIG ]
