Clyde: A Deep Reinforcement Learning DOOM Playing Agent
In this paper we present the use of deep reinforcement learning techniques in the context of playing partially observable multi agent 3D games. These techniques have traditionally been applied to fully observable 2D environments, or navigation tasks in 3D environments. We show the performance of Clyde in comparison to other competitors within the context of the ViZDOOM competition that saw 9 bots compete against each other in DOOM death matches. Clyde managed to achieve 3rd place in the ViZDOOM competition held at the IEEE Conference on Computational Intelligence and Games 2016. Clyde performed very well considering its relative simplicity and the fact that we deliberately avoided a high level of customisation to keep the algorithm generic.
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