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.

中文翻译：

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Triple-GAIL：具有生成对抗网络的多模态模仿学习框架

生成对抗模仿学习（GAIL）通过利用生成对抗网络显示出可喜的结果，特别是在机器人学习领域。然而，隔离单模态演示的要求限制了该方法对现实世界场景的可扩展性，例如自动驾驶汽车对正确理解人类驾驶员行为的需求。在本文中，我们提出了一种新的多模态 GAIL 框架，名为 Triple-GAIL，它能够通过引入辅助技能选择器，从专家演示和持续生成的数据增强经验中联合学习技能选择和模仿。我们分别为生成器和选择器的收敛性提供了理论保证。