• Corpus ID: 222291172

Efficient Wasserstein Natural Gradients for Reinforcement Learning

@article{Moskovitz2021EfficientWN,
  title={Efficient Wasserstein Natural Gradients for Reinforcement Learning},
  author={Theodore H. Moskovitz and Michal Arbel and Ferenc Husz{\'a}r and Arthur Gretton},
  journal={ArXiv},
  year={2021},
  volume={abs/2010.05380}
}
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally efficient Wasserstein natural gradient (WNG) descent that takes advantage of the geometry induced by a Wasserstein penalty to speed optimization. This method follows the recent theme in RL of including a divergence penalty in the objective to establish a trust region. Experiments on challenging tasks demonstrate… 

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