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Imitation from observation is a computational technique that teaches an agent on how to mimic the behavior of an expert by observing only the sequence of states from the expert demonstrations.
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Y. Liu, A. Gupta, P. Abbeel, and S. Levine, “Imitation from observation: Learning to imitate behaviors from raw video via context translation,” in Proceedings of ICRA 2018 , 2018, pp. 1118–1125
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F. Torabi, G. Warnell, and P. Stone, “Behavioral cloning from observation,” in Proceedings of IJCAI’18 , 2018, pp. 4950–4957
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H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in Proceedings of ICML 2019 , 2019, pp. 7354–7363
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A. D. Edwards, H. Sahni, Y. Schroecker, and C. L. Isbell, “Imitating latent policies from observation,” in Proceedings of ICML 2019 , 2019, pp. 1755–1763
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2016
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A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne, “Imitation learning: A survey of learning methods,” ACM Computing Surveys , vol. 50, no. 2, pp. 21:1–21:35, 2017
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F. Torabi, G. Warnell, and P. Stone, “Generative adversarial imitation from observation,” in I3 Workshop at ICML 2019 , 2019
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F. Torabi, G. Warnell, and P. Stone, “Imitation learning from video by leveraging proprioception,” in Proceedings of IJCAI’19 , 2019, pp. 3585–3591
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