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This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces.
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B. D. Ziebart, A. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning,” in Proc. AAAI , 2008, pp. 1433–1438
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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
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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
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2012
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2012
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J. Choi and K.-E. Kim, “Nonparametric bayesian inverse reinforcement learning for multiple reward functions,” in Advances in Neural Information Processing Systems , 2012, pp. 305–313
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Q. P. Nguyen, B. K. H. Low, and P. Jaillet, “Inverse reinforcement learning with locally consistent reward functions,” in Advances in Neural Information Processing Systems , 2015, pp. 1747–1755
2015
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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
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C. Dimitrakakis and C. A. Rothkopf, “Bayesian multitask inverse reinforcement learning,” in European Workshop on Reinforcement Learning . Springer, 2011, pp. 273–284
2011
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2015
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2016
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K. Li and J. W. Burdick, “Bellman Gradient Iteration for Inverse Reinforcement Learning,” ArXiv e-prints , Jul. 2017
2017
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