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We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals.
Theory of reproducing kernels
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D · 2011
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A universally consistent spectral estimator for the support of a distribution
De Vito, E., Rosasco, L., and Toigo, A · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Learning continuous control policies by stochastic value gradients
Heess, N., Wayne, G., Silver, D., Lillicrap, T., Erez, T., and Tassa, Y · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P · 2015
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End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al · 2016
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End-to-end differentiable adversarial imitation learning
Baram, N., Anschel, O., Caspi, I., and Mannor, S · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., and Levine, S · 2017
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Regularized kernel algorithms for support estimation
Rudi, A., De Vito, E., Verri, A., and Odone, F · 2017
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Deeply aggrevated: Differentiable imitation learning for sequential prediction
Sun, W., Venkatraman, A., Gordon, G. J., Boots, B., and Bagnell, J. A · 2017
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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Bansal, M., Krizhevsky, A., and Ogale, A · 2018
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Finn, C., Christiano, P., Abbeel, P., and Levine, S · 2016
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Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2017
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Exploration by random network distillation
Burda, Y., Edwards, H., Storkey, A., and Klimov, O · 2018
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Imitation learning via kernel mean embedding
Kim, K.-E. and Park, H. S · 2018
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
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