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The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal.
Markov decision processes
M. L. Puterman · 1990
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Efficient training of artificial neural networks for autonomous navigation
D. A. Pomerleau · 1991
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Actor-critic algorithms
V. R. Konda and J. N. Tsitsiklis · 2000
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Algorithms for inverse reinforcement learning
A. Y. Ng, S. J. Russell, et al · 2000
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Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Y. Ng · 2004
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Robust constrained model predictive control
A. G. Richards · 2005
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Maximum margin planning
N. D. Ratliff, J. A. Bagnell, and M. A. Zinkevich · 2006
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Natural actor-critic
J. Peters and S. Schaal · 2008
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Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey · 2008
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Learning to search: Functional gradient techniques for imitation learning
N. D. Ratliff, D. Silver, and J. A. Bagnell · 2009
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Efficient reductions for imitation learning
S. Ross and D. Bagnell · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Model predictive control
E. F. Camacho and C. B. Alba · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Robust bayesian inverse reinforcement learning with sparse behavior noise
J. Zheng, S. Liu, and L. M. Ni · 2014
Cited alongside, same era.
Trust region policy optimization
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz · 2015
Cited alongside, same era.
Model-based adversarial imitation learning
N. Baram, O. Anschel, and S. Mannor · 2016
Cited alongside, same era.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Cited alongside, same era.
Transfer from simulation to real world through learning deep inverse dynamics model
P. Christiano, Z. Shah, I. Mordatch, J. Schneider, T. Blackwell, J. Tobin, P. Abbeel, and W. Zaremba · 2016
Cited alongside, same era.
Data-driven planning via imitation learning
S. Choudhury, M. Bhardwaj, S. Arora, A. Kapoor, G. Ranade, S. Scherer, and D. Dey · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
K. Chua, R. Calandra, R. McAllister, and S. Levine · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Learning safe policies with expert guidance
J. Huang, F. Wu, D. Precup, and Y. Cai · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
A. Nagabandi, G. Kahn, R. S. Fearing, and S. Levine · 2018
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Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
Cited alongside, same era.
Connecting generative adversarial networks and actor-critic methods
D. Pfau and O. Vinyals · 2016
Cited alongside, same era.
Unsupervised perceptual rewards for imitation learning
P. Sermanet, K. Xu, and S. Levine · 2016
Cited alongside, same era.
Learning robust rewards with adversarial inverse reinforcement learning
J. Fu, K. Luo, and S. Levine · 2017
Cited alongside, same era.
A. Grover and S. Ermon · 2017
Cited alongside, same era.
Imitating driver behavior with generative adversarial networks
A. Kuefler, J. Morton, T. Wheeler, and M. Kochenderfer · 2017
Cited alongside, same era.
Infogail: Interpretable imitation learning from visual demonstrations
Y. Li, J. Song, and S. Ermon · 2017
Cited alongside, same era.
T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P. Abbeel, and J. Peters · 2018
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Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 2018
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Causal confusion in imitation learning
P. de Haan, D. Jayaraman, and S. Levine · 2019
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Bias correction of learned generative models using likelihood-free importance weighting
A. Grover, J. Song, A. Kapoor, K. Tran, A. Agarwal, E. J. Horvitz, and S. Ermon · 2019
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Risk-sensitive generative adversarial imitation learning
J. Lacotte, M. Ghavamzadeh, Y. Chow, and M. Pavone · 2019
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Bayesian robust optimization for imitation learning
D. S. Brown, S. Niekum, and M. Petrik · 2020
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Primal wasserstein imitation learning, 2020
R. Dadashi, L. Hussenot, M. Geist, and O. Pietquin · 2020
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Deep imitative models for flexible inference, planning, and control
N. Rhinehart, R. McAllister, and S. Levine · 2020
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Hybrid imitative planning with geometric and predictive costs in offroad environments
N. Dashora, D. Shin, D. Shah, H. Leopold, D. Fan, A. Agha, N. Rhinehart, and S. Levine · 2021
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Contingencies from observations: Tractable contingency planning with learned behavior models
N. Rhinehart, J. He, C. Packer, M. A. Wright, R. McAllister, J. E. Gonzalez, and S. Levine · 2021
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