Adversarial Imitation Learning via Boosting

arXiv:2404.08513v1 Announce Type: new
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