@InProceedings{Kempka2016ViZDoom,
  author =      {Micha{ł} Kempka and Marek Wydmuch and Grzegorz Runc and Jakub Toczek and Wojciech Jaśkowski},
  title =       {ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning},
  Booktitle                = {IEEE Conference on Computational Intelligence and Games},
  year =        {2016},
  Address                  = {Santorini, Greece},
  Month                    = {Sep},
  Pages                    = {341--348},
  Publisher                = {IEEE},
  abstract =    {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.},
  keywords =    {video games, visual-based reinforcement learning, deep reinforcement learning, first-person perspective games, FPS, visual learning, neural networks},
  url =         {http://www.cs.put.poznan.pl/wjaskowski/pub/papers/Kempka2016ViZDoom.pdf},
  notes = {arXiv:1605.02097}
}