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Revision as of 22:00, 22 February 2018 by Isucholu (talk | contribs) (From [https://papers.nips.cc/paper/6709-one-shot-imitation-learning.pdf (Duan et al. 2017)] Figure 3: Comparison of different conditioning strategies. The darkest bar shows the performance of the hard-coded policy, which unsurprisingly performs the bes...)
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From (Duan et al. 2017) Figure 3: Comparison of different conditioning strategies. The darkest bar shows the performance of the hard-coded policy, which unsurprisingly performs the best most of the time. For architectures that use temporal dropout, we use an ensemble of 10 different downsampled demonstrations and average the action

distributions. Then for all architectures we use the greedy action for evaluation.

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current22:00, 22 February 2018Thumbnail for version as of 22:00, 22 February 20181,351 × 446 (69 KB)Isucholu (talk | contribs)From [https://papers.nips.cc/paper/6709-one-shot-imitation-learning.pdf (Duan et al. 2017)] Figure 3: Comparison of different conditioning strategies. The darkest bar shows the performance of the hard-coded policy, which unsurprisingly performs the bes...

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