Playing a FPS Doom Video Game with Deep Visual Reinforcement Learning
Adil Khan, Feng Jiang, Shaohui Liu, Ibrahim Omara · Automatic Control and Computer Sciences · 2019
Abstract Because of the advancement in Deep visual reinforcement learning, now autonomous game agents are allowed to perform well which often leave behind human beings by using only the raw screen pixels for making their actions or decisions. In this paper, we propose Deep Q-Network (DQN) and a Deep Recurrent Q-Learning Network (DRQN) implementation by playing the Doom video game. Our findings are based on a publication from Lample and Chaplot (2016). Deep Q-learning under two variants (DQN and DRQN) applied is presented first, then how we build an implementation of a testbed for such algorithms is described. we presented our results on a simplified game scenario(s) by showing the predicted enemy positions (game features) with the difference in performance of DQN and DRQN. Finally, unlike other existing works, we show that our proposed architecture performs better with an accuracy of almost 72% in predicting the enemy positions.