ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling

3 Jan 2018Christopher SchulzeMarcus Schulze

ViZDoom is a robust, first-person shooter reinforcement learning environment, characterized by a significant degree of latent state information. In this paper, double-Q learning and prioritized experience replay methods are tested under a certain ViZDoom combat scenario using a competitive deep recurrent Q-network (DRQN) architecture... (read more)

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