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A Bayesian Approach to Generative Adversarial Imitation Learning
2018
Neural Information Processing Systems
Generative adversarial training for imitation learning has shown promising results on high-dimensional and continuous control tasks. This paradigm is based on reducing the imitation learning problem to the density matching problem, where the agent iteratively refines the policy to match the empirical state-action visitation frequency of the expert demonstration. Although this approach can robustly learn to imitate even with scarce demonstration, one must still address the inherent challenge
dblp:conf/nips/JeonSK18
fatcat:obctzjpj2ndrhbcv3xgmix4seq