Built on

Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine learning , 3 (1), 9-44
1988
Earlier work this paper cites.
Watkins, C. J. (1989). Learning from delayed rewards. Ph.D. dissertation, University of Cambridge England
1989
Earlier work this paper cites.
Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine learning , 8 (3-4), 279-292
1992
Earlier work this paper cites.
Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning , 8 (3-4), 229-256
1992
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Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation , 9 (8), 1735-1780
1997
Earlier work this paper cites.
Sutton, R. S., McAllester, D. A., Singh, S. P., Mansour, Y., & others. (1999). Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS, 99, pp. 1057-1063
1999
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Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. Proceedings of the twenty-first international conference on Machine learning, (p. 1)
2004
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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, (pp. 1097-1105)
2012
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Similar

Karavolos, D. (2013). Q-learning with heuristic exploration in Simulated Car Racing
2013
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Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., et al. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602
Original
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Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229
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Mnih, V., Heess, N., Graves, A., & others. (2014). Recurrent models of visual attention. Advances in Neural Information Processing Systems, (pp. 2204-2212)
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Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv preprint arXiv:1511.00561

Then

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature , 521 (7553), 436-444
2015
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Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., et al. (2015). Human-level control through deep reinforcement learning. Nature , 518 (7540), 529-533
2015
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Sorokin, I., Seleznev, A., Pavlov, M., Fedorov, A., & Ignateva, A. (2015). Deep Attention Recurrent Q-Network. arXiv preprint arXiv:1512.01693
Original
2015
Later among the works it cites.
Xu, K., Ba, J., Kiros, R., Courville, A., Salakhutdinov, R., Zemel, R., et al. (2015). Show, attend and tell: Neural image caption generation with visual attention. arXiv preprint arXiv:1502.03044
Original
2015
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Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., et al. (2016). End to End Learning for Self-Driving Cars. arXiv preprint arXiv:1604.07316

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2016

End-to-End Deep Reinforcement Learning for Lane Keeping Assist

Sallab, Ahmad El, Abdou, Mohammed, Perot, Etienne et al.

Understand

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications.

There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment.
Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning.
This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks.

Built on

Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine learning , 3 (1), 9-44
1988
Earlier work this paper cites.
Watkins, C. J. (1989). Learning from delayed rewards. Ph.D. dissertation, University of Cambridge England
1989
Earlier work this paper cites.
Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine learning , 8 (3-4), 279-292
1992
Earlier work this paper cites.
Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning , 8 (3-4), 229-256
1992
Earlier work this paper cites.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation , 9 (8), 1735-1780
1997
Earlier work this paper cites.
Sutton, R. S., McAllester, D. A., Singh, S. P., Mansour, Y., & others. (1999). Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS, 99, pp. 1057-1063
1999
Earlier work this paper cites.
Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. Proceedings of the twenty-first international conference on Machine learning, (p. 1)
2004
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, (pp. 1097-1105)
2012
Earlier work this paper cites.