Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets. (arXiv:2005.10622v1 [cs.LG])

Generative adversarial imitation learning (GAIL) has shown promising results
by taking advantage of generative adversarial nets, especially in the field of
robot learning. However, the requirement of isolated single modal
demonstrations limits the scalability of the approach to real world scenarios
such as autonomous vehicles’ demand for a proper understanding of human
drivers’ behavior. In this paper, we propose a novel multi-modal GAIL
framework, named Triple-GAIL, that is able to learn skill selection and
imitation jointly from both expert demonstrations and continuously generated
experiences with data augmentation purpose by introducing an auxiliary skill
selector. We provide theoretical guarantees on the convergence to optima for
both of the generator and the selector respectively. Experiments on real driver
trajectories and real-time strategy game datasets demonstrate that Triple-GAIL
can better fit multi-modal behaviors close to the demonstrators and outperforms
state-of-the-art methods.

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