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Adversarial Imitation Learning via Boosting
April 15, 2024, 4:42 a.m. | Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kiant\'e Brantley, Wen Sun
cs.LG updates on arXiv.org arxiv.org
Abstract: Adversarial imitation learning (AIL) has stood out as a dominant framework across various imitation learning (IL) applications, with Discriminator Actor Critic (DAC) (Kostrikov et al.,, 2019) demonstrating the effectiveness of off-policy learning algorithms in improving sample efficiency and scalability to higher-dimensional observations. Despite DAC's empirical success, the original AIL objective is on-policy and DAC's ad-hoc application of off-policy training does not guarantee successful imitation (Kostrikov et al., 2019; 2020). Follow-up work such as ValueDICE (Kostrikov …
abstract actor adversarial algorithms applications arxiv boosting cs.ai cs.lg efficiency framework imitation learning improving policy sample scalability success type via
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