  Using current reinforcement learning methods, it has recently become possible
to learn to play unknown 3D games from raw pixels. In this work, we study the
challenges that arise in such complex environments, and summarize current
methods to approach these. We choose a task within the Doom game, that has not
been approached yet. The goal for the agent is to fight enemies in a 3D world
consisting of five rooms. We train the DQN and LSTM-A3C algorithms on this
task. Results show that both algorithms learn sensible policies, but fail to
achieve high scores given the amount of training. We provide insights into the
learned behavior, which can serve as a valuable starting point for further
research in the Doom domain.
