  This paper describes methods for training autonomous agents to play the game
"Doom 2" through Imitation Learning (IL) using only pixel data as input. We
also explore how Reinforcement Learning (RL) compares to IL for humanness by
comparing camera movement and trajectory data. Through behavioural cloning, we
examine the ability of individual models to learn varying behavioural traits.
We attempt to mimic the behaviour of real players with different play styles,
and find we can train agents that behave aggressively, passively, or simply
more human-like than traditional AIs. We propose these methods of introducing
more depth and human-like behaviour to agents in video games. The trained IL
agents perform on par with the average players in our dataset, whilst
outperforming the worst players. While performance was not as strong as common
RL approaches, it provides much stronger human-like behavioural traits to the
agent.
