S. Alvernaz and J. Togelius. Autoencoder-augmented neuroevolution for visual doom playing. In Computational Intelligence and Games (CIG), 2017 IEEE Conference on. IEEE, 2017. [OpenAIRE]
 C. Beattie, J. Z. Leibo, D. Teplyashin, T. Ward, M. Wainwright, H. Ku¨ttler, A. Lefrancq, S. Green, V. Valde´s, A. Sadik, et al. Deepmind lab. arXiv preprint arXiv:1612.03801, 2016.
 M. Bellemare, Y. Naddaf, J. Veness, and M. Bowling. The arcade learning environment: An evaluation platform for general agents. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
 M. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos. Unifying count-based exploration and intrinsic motivation. In Advances in Neural Information Processing Systems, pages 1471- 1479, 2016. [OpenAIRE]
 M. G. Bellemare, W. Dabney, and R. Munos. A distributional perspective on reinforcement learning. arXiv preprint arXiv:1707.06887, 2017. [OpenAIRE]
 M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling. The arcade learning environment: An evaluation platform for general agents. J. Artif. Intell. Res.(JAIR), 47:253-279, 2013. [OpenAIRE]
 Y. Bengio, J. Louradour, R. Collobert, and J. Weston. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning, pages 41-48. ACM, 2009.
 S. Bhatti, A. Desmaison, O. Miksik, N. Nardelli, N. Siddharth, and P. H. Torr. Playing doom with slam-augmented deep reinforcement learning. arXiv preprint arXiv:1612.00380, 2016. [OpenAIRE]
 N. Bhonker, S. Rozenberg, and I. Hubara. Playing snes in the retro learning environment. arXiv preprint arXiv:1611.02205, 2016. [OpenAIRE]
 M. Bogdanovic, D. Markovikj, M. Denil, and N. De Freitas. Deep apprenticeship learning for playing video games. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
 G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016. [OpenAIRE]
 C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4(1):1-43, 2012.
 Y.-H. Chang, T. Ho, and L. P. Kaelbling. All learning is local: Multiagent learning in global reward games. In NIPS, pages 807-814, 2003.
 D. S. Chaplot, G. Lample, K. M. Sathyendra, and R. Salakhutdinov. Transfer deep reinforcement learning in 3d environments: An empirical study.
 C. Chen, A. Seff, A. Kornhauser, and J. Xiao. Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE International Conference on Computer Vision, pages 2722- 2730, 2015. [OpenAIRE]