DropoutDAgger: A Bayesian Approach to Safe Imitation Learning

S. Schaal, “Learning from demonstration,” in Advances in Neural Information Processing Systems (NIPS) , 1997, pp. 1040–1046

1997

Earlier work this paper cites.

B. Price and C. Boutilier, “Accelerating reinforcement learning through implicit imitation,” Journal of Artificial Intelligence Research , vol. 19, pp. 569–629, 2003

2003

Earlier work this paper cites.

B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and Autonomous Systems , vol. 57, no. 5, pp. 469–483, 2009

2009

Earlier work this paper cites.

H. Daumé, J. Langford, and D. Marcu, “Search-based structured prediction,” Machine Learning , vol. 75, no. 3, pp. 297–325, 2009

2009

Earlier work this paper cites.

J. Kober and J. Peters, “Imitation and reinforcement learning,” IEEE Robotics Automation Magazine , vol. 17, no. 2, pp. 55–62, June 2010

2010

Earlier work this paper cites.

S. Ross and D. Bagnell, “Efficient reductions for imitation learning,” in International Conference on Artificial Intelligence and Statistics , 2010, pp. 661–668

2010

Earlier work this paper cites.

S. Ross, G. J. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in International Conference on Artificial Intelligence and Statistics , 2011, pp. 627–635

2011

Earlier work this paper cites.

S. Levine and V. Koltun, “Guided policy search,” in International Conference on Machine Learning (ICML) , 2013, pp. 1–9

2013

Cited alongside, same era.

B. Kim and J. Pineau, “Maximum mean discrepancy imitation learning,” in Robotics: Science and Systems , 2013

2013

Cited alongside, same era.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” Journal of Machine Learning , vol. 15, no. 1, pp. 1929–1958, 2014

2014

Cited alongside, same era.

Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” Available on arXiv:1506.02142 , 2015

Original

2015

Cited alongside, same era.

J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust region policy optimization,” in International Conference on Machine Learning (ICML) , 2015, pp. 1889–1897

J. Zhang and K. Cho, “Query-efficient imitation learning for end-to-end autonomous driving,” Available on arXiv:1605.06450 , 2016

Original

2016

Later among the works it cites.

R. Oliveira, P. Tabacof, and E. Valle, “Known unknowns: Uncertainty quality in bayesian neural networks,” Available on arXiv:1612.01251 , 2016

Original

2016

Later among the works it cites.

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “OpenAI Gym,” Available on arXiv:1606.01540 , 2016

Original

2016

Later among the works it cites.

Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, “Benchmarking deep reinforcement learning for continuous control,” in International Conference on Machine Learning (ICML) , 2016, pp. 1329–1338

2016

Later among the works it cites.