  Recent developments in deep reinforcement learning have enabled the creation
of agents for solving a large variety of games given a visual input. These
methods have been proven successful for 2D games, like the Atari games, or for
simple tasks, like navigating in mazes. It is still an open question, how to
address more complex environments, in which the reward is sparse and the state
space is huge. In this paper we propose a divide and conquer deep reinforcement
learning solution and we test our agent in the first person shooter (FPS) game
of Doom. Our work is based on previous works in deep reinforcement learning and
in Doom agents. We also present how our agent is able to perform better in
unknown environments compared to a state of the art reinforcement learning
algorithm.
