  Motivated by recent advance of machine learning using Deep Reinforcement
Learning this paper proposes a modified architecture that produces more robust
agents and speeds up the training process. Our architecture is based on
Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input
dimensionality is halved by dividing the input into two independent streams. We
use ViZDoom, 3D world software that is based on the classical first person
shooter video game, Doom, as a test case. The experiments show that in
comparison to single input agents, the proposed architecture succeeds to have
the same playing performance and shows more robust behavior, achieving
significant reduction in the number of training parameters of almost 30%.
