ViZDoom: A Doom based AI research platform for visual reinforcement learning
The recent advances in deep neural networks have led to effective vision based reinforcement learning methods that have been employed to obtain human level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real world tasks since they involve non realistic 2D environments and the third person perspective. Here, we propose a novel test bed platform for reinforcement learning research from raw visual information which employs the first person perspective in a semi realistic 3D world. The software, called ViZDoom, is based on the classical first person shooter video game, Doom. It allows developing bots that play the game using the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a convenient mechanism of user scenarios. In the experimental part, we test the environment by trying to learn bots for two scenarios: a basic move and shoot task and a more complex maze navigation problem. Using convolutional deep neural networks with Q learning and experience replay, for both scenarios, we were able to train competent bots, which exhibit human like behaviors. The results confirm the utility of ViZDoom as an AI research platform and imply that visual reinforcement learning in 3D realistic first person perspective environments is feasible.
2016 IEEE Conference on Computational Intelligence and Games (CIG)