Adaptive Shooting for Bots in First Person Shooter Games Using
  Reinforcement Learning
  In current state-of-the-art commercial first person shooter games, computer
controlled bots, also known as non player characters, can often be easily
distinguishable from those controlled by humans. Tell-tale signs such as failed
navigation, "sixth sense" knowledge of human players' whereabouts and
deterministic, scripted behaviors are some of the causes of this. We propose,
however, that one of the biggest indicators of non humanlike behavior in these
games can be found in the weapon shooting capability of the bot. Consistently
perfect accuracy and "locking on" to opponents in their visual field from any
distance are indicative capabilities of bots that are not found in human
players. Traditionally, the bot is handicapped in some way with either a timed
reaction delay or a random perturbation to its aim, which doesn't adapt or
improve its technique over time. We hypothesize that enabling the bot to learn
the skill of shooting through trial and error, in the same way a human player
learns, will lead to greater variation in game-play and produce less
predictable non player characters. This paper describes a reinforcement
learning shooting mechanism for adapting shooting over time based on a dynamic
reward signal from the amount of damage caused to opponents.
