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Sample-Efficient Imitation Learning via Generative Adversarial Nets
[article]
2019
arXiv
pre-print
GAIL is a recent successful imitation learning architecture that exploits the adversarial training procedure introduced in GANs. Albeit successful at generating behaviours similar to those demonstrated to the agent, GAIL suffers from a high sample complexity in the number of interactions it has to carry out in the environment in order to achieve satisfactory performance. We dramatically shrink the amount of interactions with the environment necessary to learn well-behaved imitation policies, by
arXiv:1809.02064v3
fatcat:xxihg6wl2bdy3kci6kzhoj5lli