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
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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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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
2005
Earlier work this paper cites.

Similar

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
Cited alongside, same era.
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
2013
Cited alongside, same era.
Pinheiro, P. H., & Collobert, R. (2013). Recurrent convolutional neural networks for scene parsing. arXiv preprint arXiv:1306.2795
Original
2013
Cited alongside, same era.
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
Original
2013
Cited alongside, same era.

Then

Kendall, A., Badrinarayanan, V., & Cipolla, R. (2015). Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. arXiv preprint arXiv:1511.02680
Original
2015
Later among the works it cites.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature , 521 (7553), 436-444
2015
Later among the works it cites.
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
Later among the works it cites.
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.
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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2017

Deep Reinforcement Learning framework for Autonomous Driving

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.

Despite its perceived utility, it has not yet been successfully applied in automotive applications.
Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning.
This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions 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.
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
2005
Earlier work this paper cites.

Similar

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
Cited alongside, same era.
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
2013
Cited alongside, same era.
Pinheiro, P. H., & Collobert, R. (2013). Recurrent convolutional neural networks for scene parsing. arXiv preprint arXiv:1306.2795
Original
2013
Cited alongside, same era.
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
Original
2013
Cited alongside, same era.

Then

Kendall, A., Badrinarayanan, V., & Cipolla, R. (2015). Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. arXiv preprint arXiv:1511.02680
Original
2015
Later among the works it cites.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature , 521 (7553), 436-444
2015
Later among the works it cites.
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
Later among the works it cites.
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.
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

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Mnih, V., Heess, N., Graves, A., & others. (2014). Recurrent models of visual attention. Advances in Neural Information Processing Systems, (pp. 2204-2212)

2014

Cited alongside, same era.

Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv preprint arXiv:1511.00561

Original

2015

Cited alongside, same era.

Hausknecht, M., & Stone, P. (2015). Deep recurrent q-learning for partially observable mdps. arXiv preprint arXiv:1507.06527

Original

2015

Cited alongside, same era.

Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., et al. (2015). An Empirical Evaluation of Deep Learning on Highway Driving. arXiv preprint arXiv:1504.01716

Original