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Using current reinforcement learning methods, it has recently become possibleto learn to play unknown 3D games from raw pixels. In this work, we study thechallenges that arise in such complex environments, and summarize currentmethods to approach these. We choose a task within the Doom game, that has notbeen approached yet. The goal for the agent is to fight enemies in a 3D worldconsisting of five rooms. We train the DQN and LSTM-A3C algorithms on thistask. Results show that both algorithms learn sensible policies, but fail toachieve high scores given the amount of training. We provide insights into thelearned behavior, which can serve as a valuable starting point for furtherresearch in the Doom domain.

Deep Reinforcement Learning From Raw Pixels in Doom