Built on

R. M. Neal, “Bayesian learning for neural networks.” PhD Thesis, University of Toronto, 1995
1995
Earlier work this paper cites.
P.-T. de Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, “A Tutorial on the Cross-Entropy Method,” Annals of Operations Research , vol. 134, no. 1, pp. 19–67, feb 2005. [Online]. Available: http://link.springer.com/10.1007/s10479-005-5724-z
2005
Earlier work this paper cites.
C. M. Bishop, Pattern recognition and machine learning . Springer, 2006
2006
Earlier work this paper cites.
Y. Tassa, T. Erez, and W. D. Smart, “Receding horizon differential dynamic programming,” in Advances in Neural Information Processing Systems 20 , J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, Eds. Curran Associates, Inc., 2008, pp. 1465–1472. [Online]. Available: http://papers.nips.cc/paper/3297-receding-horizon-differential-dynamic-programming.pdf
2008
Earlier work this paper cites.
S. Ross, G. J. Gordon, and J. A. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics , ser. JMLR, vol. 15, Fort Lauderdale, FL, USA, 2011. [Online]. Available: http://proceedings.mlr.press/v15/ross11a/ross11a.pdf
2011
Earlier work this paper cites.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” jul 2012. [Online]. Available: http://arxiv.org/abs/1207.0580
Original
2012
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.

Similar

C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural network,” in Proceedings of the 32nd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, F. Bach and D. Blei, Eds., vol. 37. Lille, France: PMLR, 07–09 Jul 2015, pp. 1613–1622. [Online]. Available: http://proceedings.mlr.press/v37/blundell15.html
2015
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in Proceedings of The 33rd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 48. New York, New York, USA: PMLR, 20–22 Jun 2016, pp. 1050–1059. [Online]. Available: http://proceedings.mlr.press/v48/gal16.html
2016
Cited alongside, same era.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016. [Online]. Available: https://arxiv.org/abs/1606.01540
Original
2016
Cited alongside, same era.

Then

M. Bojarski, P. Yeres, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, and U. Muller, “Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car,” apr 2017. [Online]. Available: http://arxiv.org/abs/1704.07911
Original
2017
Later among the works it cites.
M. Strickland, G. E. Fainekos, and H. B. Amor, “Deep predictive models for collision risk assessment in autonomous driving,” CoRR , vol. abs/1711.10453, 2017. [Online]. Available: http://arxiv.org/abs/1711.10453
Original
2017
Later among the works it cites.
K. Menda, K. Driggs-Campbell, and M. J. Kochenderfer, “Dropoutdagger: A bayesian approach to safe imitation learning,” 09 2017. [Online]. Available: https://arxiv.org/abs/1709.06166
Original
2017
Later among the works it cites.
Y. Gal, J. Hron, and A. Kendall, “Concrete dropout,” in Advances in Neural Information Processing Systems 30 (NIPS) , 2017. [Online]. Available: https://arxiv.org/abs/1705.07832
Original
2017
Later among the works it cites.

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2018

Safe end-to-end imitation learning for model predictive control

Lee, Keuntaek, Saigol, Kamil, Theodorou, Evangelos A.

Understand

We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set.

Our algorithm combines reinforcement learning and end-to-end imitation learning to simultaneously learn a control policy as well as a threshold over the predictive uncertainty of the learned model, with no hand-tuning required.
Corrective action, such as a return of control to the model predictive controller or human expert, is taken when the uncertainty threshold is exceeded.
We validate our method on fully-observable and vision-based partially-observable systems using cart-pole and autonomous driving simulations using deep convolutional Bayesian neural networks.

Built on

R. M. Neal, “Bayesian learning for neural networks.” PhD Thesis, University of Toronto, 1995
1995
Earlier work this paper cites.
P.-T. de Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, “A Tutorial on the Cross-Entropy Method,” Annals of Operations Research , vol. 134, no. 1, pp. 19–67, feb 2005. [Online]. Available: http://link.springer.com/10.1007/s10479-005-5724-z
2005
Earlier work this paper cites.
C. M. Bishop, Pattern recognition and machine learning . Springer, 2006
2006
Earlier work this paper cites.
Y. Tassa, T. Erez, and W. D. Smart, “Receding horizon differential dynamic programming,” in Advances in Neural Information Processing Systems 20 , J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, Eds. Curran Associates, Inc., 2008, pp. 1465–1472. [Online]. Available: http://papers.nips.cc/paper/3297-receding-horizon-differential-dynamic-programming.pdf
2008
Earlier work this paper cites.
S. Ross, G. J. Gordon, and J. A. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics , ser. JMLR, vol. 15, Fort Lauderdale, FL, USA, 2011. [Online]. Available: http://proceedings.mlr.press/v15/ross11a/ross11a.pdf
2011
Earlier work this paper cites.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” jul 2012. [Online]. Available: http://arxiv.org/abs/1207.0580
Original
2012
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR , vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
Original
2014
Earlier work this paper cites.

Similar

C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural network,” in Proceedings of the 32nd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, F. Bach and D. Blei, Eds., vol. 37. Lille, France: PMLR, 07–09 Jul 2015, pp. 1613–1622. [Online]. Available: http://proceedings.mlr.press/v37/blundell15.html
2015
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in Proceedings of The 33rd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 48. New York, New York, USA: PMLR, 20–22 Jun 2016, pp. 1050–1059. [Online]. Available: http://proceedings.mlr.press/v48/gal16.html
2016
Cited alongside, same era.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016. [Online]. Available: https://arxiv.org/abs/1606.01540
Original
2016
Cited alongside, same era.

Then

M. Bojarski, P. Yeres, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, and U. Muller, “Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car,” apr 2017. [Online]. Available: http://arxiv.org/abs/1704.07911
Original
2017
Later among the works it cites.
M. Strickland, G. E. Fainekos, and H. B. Amor, “Deep predictive models for collision risk assessment in autonomous driving,” CoRR , vol. abs/1711.10453, 2017. [Online]. Available: http://arxiv.org/abs/1711.10453
Original
2017
Later among the works it cites.
K. Menda, K. Driggs-Campbell, and M. J. Kochenderfer, “Dropoutdagger: A bayesian approach to safe imitation learning,” 09 2017. [Online]. Available: https://arxiv.org/abs/1709.06166
Original
2017
Later among the works it cites.
Y. Gal, J. Hron, and A. Kendall, “Concrete dropout,” in Advances in Neural Information Processing Systems 30 (NIPS) , 2017. [Online]. Available: https://arxiv.org/abs/1705.07832
Original
2017
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

“Autorally,” http://autorally.github.io/ , 2016

2016

Cited alongside, same era.

Y. Pan, X. Yan, E. Theodorou, and B. Boots., “Adaptive probabilistic trajectory optimization via efficient approximate inference,” in 29th Conference on Neural Information Processing Systems (NIPS) , Barcelona, Spain, 2016. [Online]. Available: https://arxiv.org/pdf/1608.06235.pdf

Original

2016

Cited alongside, same era.

Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. Theodorou, and B. Boots., “Learning deep neural network control policies for agile off-road autonomous driving,” in The NIPS Deep Rienforcement Learning Symposium , 2017. [Online]. Available: https://www.cc.gatech.edu/~bboots3/files/nips17drl.pdf

2017

Cited alongside, same era.

A. Kendall and Y. Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” mar 2017. [Online]. Available: http://arxiv.org/abs/1703.04977

Original

2017

Later among the works it cites.

M. Pereira, D. D. Fan, G. N. An, and E. Theodorou, “MPC-Inspired Neural Network Policies for Sequential Decision Making,” feb 2018. [Online]. Available: http://arxiv.org/abs/1802.05803

Original

2018

Closest in time.