publication . Preprint . 2017

Autoencoder-augmented Neuroevolution for Visual Doom Playing

Alvernaz, Samuel; Togelius, Julian;
Open Access English
  • Published: 12 Jul 2017
Comment: IEEE conference on Computational Intelligence and Games 2017
free text keywords: Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Download from
29 references, page 1 of 2

[1] Gabriel Barth-Maron. Learning deep state representations with convolutional autoencoders. Master's thesis, Brown University.

[2] Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N Siddharth, and Philip HS Torr. Playing doom with slam-augmented deep reinforcement learning. arXiv preprint arXiv:1612.00380, 2016.

[3] Giuseppe Cuccu, Matthew Luciw, Ju¨ rgen Schmidhuber, and Faustino Gomez. Intrinsically motivated neuroevolution for vision-based reinforcement learning. In Development and Learning (ICDL), 2011 IEEE International Conference on, volume 2, pages 1-7. IEEE, 2011.

[4] Alexey Dosovitskiy and Vladlen Koltun. Learning to act by predicting the future. arXiv preprint arXiv:1611.01779, 2016. [OpenAIRE]

[5] Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. Why does unsupervised pretraining help deep learning? Journal of Machine Learning Research, 11(Feb):625-660, 2010.

[6] Dario Floreano, Peter Du¨ rr, and Claudio Mattiussi. Neuroevolution: from architectures to learning. Evolutionary Intelligence, 1(1):47-62, 2008.

[7] Faustino Gomez, J u¨rgen Schmidhuber, and Risto Miikkulainen. Efficient non-linear control through neuroevolution. In European Conference on Machine Learning, pages 654-662. Springer, 2006. [OpenAIRE]

[8] Nikolaus Hansen. The cma evolution strategy: a comparing review. In Towards a new evolutionary computation, pages 75-102. Springer, 2006.

[9] Nikolaus Hansen and Andreas Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, 9(2):159-195, 2001.

[10] Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. science, 313(5786):504-507, 2006.

[11] Christian Igel. Neuroevolution for reinforcement learning using evolution strategies. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on, volume 4, pages 2588-2595. IEEE, 2003.

[12] Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jas´kowski. Vizdoom: A doom-based ai research platform for visual reinforcement learning. In IEEE Conference on Computational Intelligence and Games, 2016. [OpenAIRE]

[13] Jan Koutn´ık, Giuseppe Cuccu, Ju¨ rgen Schmidhuber, and Faustino Gomez. Evolving large-scale neural networks for vision-based reinforcement learning. In Proceedings of the 15th annual conference on Genetic and evolutionary computation, pages 1061-1068. ACM, 2013.

[14] Jan Koutn´ık, Ju¨ rgen Schmidhuber, and Faustino Gomez. Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pages 541-548. ACM, 2014.

[15] Guillaume Lample and Devendra Singh Chaplot. Playing fps games with deep reinforcement learning. arXiv preprint arXiv:1609.05521, 2016.

29 references, page 1 of 2
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue