State Aware Imitation Learning

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Primal Wasserstein Imitation Learning (2020, background): “…estimation Another line of research in IL consists in estimating the state-action support of the expert and deﬁne a reward that encourages the agent to stay on the support of the expert [Piot et al., 2014, Schroecker and Isbell, 2017, Wang et al., 2019, Brantley et al., 2020, Reddy et al., 2020].”
Generative predecessor models for sample-efficient imitation learning (2019, methodology): “In this work, we follow a gradient descent approach similar to SAIL but estimate the gradient without representing it explicitly by a neural network.”
Imitating Latent Policies from Observation (2018, background): “monstrations and uses adversarial training to distinguish if a state-action pair comes from following the agent or expert’s policy while simultaneously minimizing the difference between the two. SAIL [21] achieves a similar goal by using temporal difference learning to estimate the gradient of the n”