doi:10.1017/S0269888919000055

Abtahi , F. & Fasel , I. 2011. Deep belief nets as function approximators for reinforcement learning, Restricted Boltzmann Machine (RBM) 2, h3.

Anderson , C. W., Lee , M. & Elliott , D. L. 2015. Faster reinforcement learning after pretraining deep networks to predict state dynamics. In 2015 International Joint Conference on Neural Networks (IJCNN), 1–7. IEEE.

Argall , B. D., Chernova , S., Veloso , M. & Browning , B. 2009. A survey of robot learning from demonstration. Robotics and Autonomous Systems 57(5), 469–483.

Bellemare , M. G., Naddaf , Y., Veness , J. & Bowling , M. 2013. The arcade learning environment: an evaluation platform for general agents. Journal of Artificial Intelligence Research 47, 253–279.

Bojarski , M., Yeres , P., Choromanska , A., Choromanski , K., Firner , B., Jackel , L. & Muller , U. 2017. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911.

Brockman , G., Cheung , V., Pettersson , L., Schneider , J., Schulman , J., Tang , J. & Zaremba , W. 2016. Openai gym.

Brys , T., Harutyunyan , A., Taylor , M. E. & Nowé , A. 2015. Policy transfer using reward shaping. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 181–188. International Foundation for Autonomous Agents and Multiagent Systems.

Christiano , P., Leike , J., Brown , T. B., Martic , M., Legg , S. & Amodei , D. 2017. Deep reinforcement learning from human preferences. In NIPS, Curran Associates, Inc.

Deng , Y., Bao , F., Kong , Y., Ren , Z. & Dai , Q. 2017. Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems 28(3), 653–664.

Dhariwal , P., Hesse , C., Klimov , O., Nichol , A., Plappert , M., Radford , A., Schulman , J., Sidor , S., Wu , Y. & Zhokhov , P. 2017. Openai baselines. https://github.com/openai/baselines.

Du , Y., Czarnecki , W. M., Jayakumar , S. M., Pascanu , R. & Lakshminarayanan , B. 2018. Adapting auxiliary losses using gradient similarity. arXiv preprint arXiv:1812.02224.

Du , Y., de la Cruz , Jr., G. V., Irwin , J. & Taylor , M. E. 2016. Initial progress in transfer for deep reinforcement learning algorithms. In Proceedings of the Deep Reinforcement Learning: Frontiers and Challenges (DeepRL) Workshop (at IJCAI 2016).

Duan , Y., Chen , X., Houthooft , R., Schulman , J. & Abbeel , P. 2016. Benchmarking deep reinforcement learning for continuous control. In International Conference on Machine Learning, 1329–1338, JMLR.org.

Erhan , D., Bengio , Y., Courville , A., Manzagol , P.-A., Vincent , P. & Bengio , S. 2010. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research 11, 625–660.

Erhan , D., Manzagol , P.-A., Bengio , Y., Bengio , S. & Vincent , P. 2009. The difficulty of training deep architectures and the effect of unsupervised pre-training. In Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 153–160, PMLR.

Glatt , R., d. Silva , F. L. & Costa , A. H. R. 2016. Towards knowledge transfer in deep reinforcement learning. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 91–96, IEEE.

Hester , T., Vecerik , M., Pietquin , O., Lanctot , M., Schaul , T., Piot , B., Sendonaris , A., Dulac-Arnold , G., Osband , I., Agapiou , J., Leibo , J. Z. & Gruslys , A. 2018. Deep q-learning from demonstrations. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, AAAI Press.

Jaderberg , M., Mnih , V., Czarnecki , W. M., Schaul , T., Leibo , J. Z., Silver , D. & Kavukcuoglu , K. 2017. Reinforcement learning with unsupervised auxiliary tasks. In ICLR, OpenReview.net.

Kempka , M., Wydmuch , M., Runc , G., Toczek , J. & Jaśkowski , W. 2016. Vizdoom: a Doom-based AI research platform for visual reinforcement learning. In 2016 IEEE Conference on Computational Intelligence and Games (CIG), 1–8. IEEE.

Kurin , V., Nowozin , S., Hofmann , K., Beyer , L. & Leibe , B. 2017. The Atari grand challenge dataset. arXiv preprint arXiv:1705.10998.

Lillicrap , T. P., Hunt , J. J., Pritzel , A., Heess , N., Erez , T., Tassa , Y., Silver , D. & Wierstra , D. 2016. Continuous control with deep reinforcement learning. In ICLR, OpenReview.net.

Lin , M., Chen , Q. & Yan , S. 2013. Network in network. arXiv preprint arXiv:1312.4400.

Miotto , R., Wang , F., Wang , S., Jiang , X. & Dudley , J. T. 2017. Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics 19(6), 1236–1246.

Mirowski , P., Pascanu , R., Viola , F., Soyer , H., Ballard , A. J., Banino , A., Denil , M., Goroshin , R., Sifre , L., Kavukcuoglu , K., Kumaran , D. & Hadsell , R. 2017. Learning to navigate in complex environments. In ICLR, OpenReview.net.

Mnih , V., Badia , A. P., Mirza , M., Graves , A., Lillicrap , T., Harley , T., Silver , D. & Kavukcuoglu , K. 2016. Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning, 1928–1937, JMLR.org.

Mnih , V., Kavukcuoglu , K., Silver , D., Rusu , A. A., Veness , J., Bellemare , M. G., Graves , A., Riedmiller , M., Fidjeland , A. K., Ostrovski , G., Petersen , S., Beattie , C., Sadik , A., Antonoglou , I., King , H., Kumaran , D., Wierstra , D., Legg , S. & Hassabis , D. 2015. Human-level control through deep reinforcement learning. Nature 518(7540), 529–533, Nature Publishing Group.

Ng , A. Y., Harada , D. & Russell , S. 1999. Policy invariance under reward transformations: theory and application to reward shaping. In ICML, 99, 278–287, Morgan Kaufmann.

Pan , S. J. & Yang , Q. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359.

Papoudakis , G., Chatzidimitriou , K. C. & Mitkas , P. A. 2018. Deep reinforcement learning for Doom using unsupervised auxiliary tasks. CoRR abs/1807.01960.

Parisotto , E., Ba , J. L. & Salakhutdinov , R. 2016. Actor-mimic: deep multitask and transfer reinforcement learning. In ICLR, OpenReview.net.

Pohlen , T., Piot , B., Hester , T., Azar , M. G., Horgan , D., Budden , D., Barth-Maron , G., van Hasselt , H., Quan , J., Večerík , M., Hessel , M., Munos , R. & Pietquin , O. 2018. Observe and look further: achieving consistent performance on Atari. arXiv preprint arXiv:1805.11593.

Russakovsky , O., Deng , J., Su , H., Krause , J., Satheesh , S., Ma , S., Huang , Z., Karpathy , A., Khosla , A., Bernstein , M., Alexander , C. B. & Fei-Fei , L. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision 115(3), 211–252.

Rusu , A. A., Colmenarejo , S. G., Gulcehre , C., Desjardins , G., Kirkpatrick , J., Pascanu , R., Mnih , V., Kavukcuoglu , K. & Hadsell , R. 2016, Policy distillation. In ICLR, OpenReview.net.

Selvaraju , R. R., Cogswell , M., Das , A., Vedantam , R., Parikh , D. & Batra , D. 2017. Grad-cam: visual explanations from deep networks via gradient-based localization. In ICCV, 618–626, IEEE.

Silver , D., Huang , A., Maddison , C. J., Guez , A., Sifre , L., Van Den Driessche , G., Schrittwieser , J., Antonoglou , I., Panneershelvam , V., Lanctot , M., Dieleman , S., Grewe , D., Nham , J., Kalchbrenner , N., Sutskever , I., Lillicrap , T., Leach , M., Kavukcuoglu , K., Graepel , T. & Hassabis , D. 2016. Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489.

Silver , D., Hubert , T., Schrittwieser , J., Antonoglou , I., Lai , M., Guez , A., Lanctot , M., Sifre , L., Kumaran , D., Graepel , T., Lillicrap , T., Simonyan , K. & Hassabis , D. 2018. A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science 362(6419), 1140–1144.

Sutton , R. S. & Barto , A. G. 2018. Reinforcement Learning: An Introduction. MIT Press.

Taylor , M. E. & Stone , P. 2009. Transfer learning for reinforcement learning domains: a survey. Journal of Machine Learning Research 10(Jul), 1633–1685.

Teh , Y., Bapst , V., Czarnecki , W. M., Quan , J., Kirkpatrick , J., Hadsell , R., Heess , N. & Pascanu , R. 2017. Distral: robust multitask reinforcement learning. In Advances in Neural Information Processing Systems, 4496–4506.

Tieleman , T. & Hinton , G. 2012. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning 4(2), 26–31.

Vinyals , O., Ewalds , T., Bartunov , S., Georgiev , P., Vezhnevets , A. S., Yeo , M., Makhzani , A., Küttler , H., Agapiou , J., Schrittwieser , J., Quan , J., Gaffney , S., Petersen , S., Simonyan , K., Schaul , T., van Hasselt , H., Silver , D., Lillicrap , T., Calderone , K., Keet , P., Brunasso , A., Lawrence , D., Ekermo , A., Repp , J. & Tsing , R. 2017. Starcraft II: a new challenge for reinforcement learning. arXiv preprint arXiv:1708.04782.

Wang , Z. & Taylor , M. E. 2017. Improving reinforcement learning with confidence-based demonstrations. In Proceedings of the 26th International Conference on Artificial Intelligence (IJCAI), International Joint Conference on Artificial Intelligence.

Watkins , C. J. & Dayan , P. 1992. Q-learning. Machine Learning 8(3–4), 279–292.

Yosinski , J., Clune , J., Bengio , Y. & Lipson , H. 2014. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems, 3320–3328.

Zhang , Y., Lee , K. & Lee , H. 2016. Augmenting supervised neural networks with unsupervised objectives for large-scale image classification. In ICML, JMLR.org.