Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis

R. Kalman and M. M. C. B. D. R. I. for Advanced Studies. Center for Control Theory, When is a Linear Control System Optimal?. , ser. RIAS technical report. Martin Marietta Corporation, Research Institute for Advanced Studies, Center for Control Theory, 1963

1963

Earlier work this paper cites.

R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction . MIT press Cambridge, 1998, vol. 1, no. 1

1998

Earlier work this paper cites.

A. Y. Ng and S. Russell, “Algorithms for inverse reinforcement learning,” in in Proc. 17th International Conf. on Machine Learning , 2000

2000

Earlier work this paper cites.

R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning,” Artificial Intelligence Review , vol. 18, no. 2, pp. 77–95, 2002

2002

Earlier work this paper cites.

B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew, C. J. Bula, and P. Robert, “Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly,” IEEE Transactions on biomedical Engineering , vol. 50, no. 6, pp. 711–723, 2003

2003

Earlier work this paper cites.

N. Schweighofer and K. Doya, “Meta-learning in reinforcement learning,” Neural Networks , vol. 16, no. 1, pp. 5–9, 2003

2003

Earlier work this paper cites.

P. Abbeel and A. Y. Ng, “Apprenticeship learning via inverse reinforcement learning,” in Proceedings of the twenty-first international conference on Machine learning . ACM, 2004, p. 1

2004

Earlier work this paper cites.

N. D. Ratliff, J. A. Bagnell, and M. A. Zinkevich, “Maximum margin planning,” in Proceedings of the 23rd international conference on Machine learning . ACM, 2006, pp. 729–736

2006

Earlier work this paper cites.

D. Ramachandran and E. Amir, “Bayesian inverse reinforcement learning,” in Proceedings of the 20th International Joint Conference on Artifical Intelligence , ser. IJCAI’07. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2007, pp. 2586–2591

2007

Earlier work this paper cites.

B. D. Ziebart, A. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning,” in Proc. AAAI , 2008, pp. 1433–1438

2008

Earlier work this paper cites.

K. Mombaur, A. Truong, and J.-P. Laumond, “From human to humanoid locomotion—an inverse optimal control approach,” Autonomous robots , vol. 28, no. 3, pp. 369–383, 2010

2010

Earlier work this paper cites.

S. Levine, Z. Popovic, and V. Koltun, “Nonlinear inverse reinforcement learning with gaussian processes,” in Advances in Neural Information Processing Systems 24 , J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2011, pp. 19–27

2011

Cited alongside, same era.

J. Choi and K.-E. Kim, “Inverse reinforcement learning in partially observable environments,” Journal of Machine Learning Research , vol. 12, no. Mar, pp. 691–730, 2011

2011

Cited alongside, same era.

C. Dimitrakakis and C. A. Rothkopf, “Bayesian multitask inverse reinforcement learning,” in European Workshop on Reinforcement Learning . Springer, 2011, pp. 273–284

2011

Cited alongside, same era.

A. Boularias, J. Kober, and J. R. Peters, “Relative entropy inverse reinforcement learning,” in International Conference on Artificial Intelligence and Statistics , 2011, pp. 182–189

2011

Cited alongside, same era.

C. Finn, S. Levine, and P. Abbeel, “Guided cost learning: Deep inverse optimal control via policy optimization,” arXiv preprint arXiv:1603.00448 , 2016

Original

2016

Later among the works it cites.

A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, “Meta-learning with memory-augmented neural networks,” in International conference on machine learning , 2016, pp. 1842–1850

2016

Later among the works it cites.

B. Hariharan and R. Girshick, “Low-shot visual recognition by shrinking and hallucinating features,” arXiv preprint arXiv:1606.02819 , 2016

Original

2016

Later among the works it cites.

M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas, “Learning to learn by gradient descent by gradient descent,” in Advances in Neural Information Processing Systems , 2016, pp. 3981–3989

2016

Later among the works it cites.