Wasserstein Adversarial Imitation Learning

Wasserstein Adversarial Imitation Learning

Wasserstein Adversarial Imitation Learning

Huang Xiao, Michael Herman, Joerg Wagner, Sebastian Ziesche, Jalal Etesami, Thai Hong Linh

arXiv preprint arXiv:1906.08113, June 19, 2019


Imitation Learning describes the problem of recovering an expert policy from demonstrations. While inverse reinforcement learning approaches are known to be very sample-efficient in terms of expert demonstrations, they usually require problem-dependent reward functions or a (task-) specific reward-function regularization. In this paper, we show a natural connection between inverse reinforcement learning approaches and Optimal Transport, that enables more general reward functions with desirable properties (eg, smoothness). Based on our observation, we propose a novel approach called Wasserstein Adversarial Imitation Learning. Our approach considers the Kantorovich potentials as a reward function and further leverages regularized optimal transport to enable large-scale applications. In several robotic experiments, our approach outperforms the baselines in terms of average cumulative rewards and shows a significant improvement in sample-efficiency, by requiring just one expert demonstration.

  title={Wasserstein adversarial imitation learning},
  author={Xiao, Huang and Herman, Michael and Wagner, Joerg and Ziesche, Sebastian and Etesami, Jalal and Linh, Thai Hong},
  journal={arXiv preprint arXiv:1906.08113},

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