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Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a reward function justifying the behavior demonstrated by an expert agent.
Good coverings of hamming spaces with spheres
Cohen, G. D. and Frankl, P · 1985
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
Puterman, M. L · 1994
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Variational Analysis , volume 317 of Grundlehren der mathematischen Wissenschaften
Rockafellar, R. T. and Wets, R. J · 1998
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Reinforcement learning - an introduction
Sutton, R. S. and Barto, A. G · 1998
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Algorithms for inverse reinforcement learning
Ng, A. Y. and Russell, S · 2000
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A Distribution-Free Theory of Nonparametric Regression
Györfi, L., Kohler, M., Krzyzak, A., and Walk, H · 2002
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Inequalities for the l1 deviation of the empirical distribution
Weissman, T., Ordentlich, E., Seroussi, G., Verdu, S., and Weinberger, M. J · 2003
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Apprenticeship learning via inverse reinforcement learning
Abbeel, P. and Ng, A. Y · 2004
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Maximum margin planning
Ratliff, N. D., Bagnell, J. A., and Zinkevich, M · 2006
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A game-theoretic approach to apprenticeship learning
Syed, U. and Schapire, R. E · 2007
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Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., and Dey, A. K · 2008
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Maximum likelihood inverse reinforcement learning
Vroman, M. C · 2014
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Sample complexity of episodic fixed-horizon reinforcement learning, 2015
Dann, C. and Brunskill, E · 2015
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Inverse reinforcement learning through policy gradient minimization
Pirotta, M. and Restelli, M · 2016
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Fano’s inequality for random variables
Gerchinovitz, S., Ménard, P., and Stoltz, G · 2017
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Compatible reward inverse reinforcement learning
Metelli, A. M., Pirotta, M., and Restelli, M · 2017
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An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., and Peters, J · 2018
A survey of inverse reinforcement learning: Challenges, methods and progress
Arora, S. and Doshi, P · 2021
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Inverse reinforcement learning in a continuous state space with formal guarantees
Dexter, G., Bello, K., and Honorio, J · 2021
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Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited
Domingues, O. D., Ménard, P., Kaufmann, E., and Valko, M · 2021
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Adaptive reward-free exploration
Kaufmann, E., Ménard, P., Domingues, O. D., Jonsson, A., Leurent, E., and Valko, M · 2021
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A lower bound for the sample complexity of inverse reinforcement learning
Komanduru, A. and Honorio, J · 2021
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Fast active learning for pure exploration in reinforcement learning
Ménard, P., Domingues, O. D., Jonsson, A., Kaufmann, E., Leurent, E., and Valko, M · 2021
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On the correctness and sample complexity of inverse reinforcement learning
Komanduru, A. and Honorio, J · 2019
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Reward-free exploration for reinforcement learning
Jin, C., Krishnamurthy, A., Simchowitz, M., and Yu, T · 2020
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Planning in markov decision processes with gap-dependent sample complexity
Jonsson, A., Kaufmann, E., Ménard, P., Domingues, O. D., Leurent, E., and Valko, M · 2020
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Bandit algorithms
Lattimore, T. and Szepesvári, C · 2020
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Truly batch model-free inverse reinforcement learning about multiple intentions
Ramponi, G., Likmeta, A., Metelli, A. M., Tirinzoni, A., and Restelli, M · 2020
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Provably efficient learning of transferable rewards
Metelli, A. M., Ramponi, G., Concetti, A., and Restelli, M · 2021
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A survey of inverse reinforcement learning
Adams, S. C., Cody, T., and Beling, P. A · 2022
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Active exploration for inverse reinforcement learning
Lindner, D., Krause, A., and Ramponi, G · 2022
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A survey of human-in-the-loop for machine learning
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., and He, L · 2022
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Maximum-likelihood inverse reinforcement learning with finite-time guarantees
Zeng, S., Li, C., Garcia, A., and Hong, M · 2022
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