Moskovitz, Ted;
Arbel, Michael;
Huszar, Ferenc;
Gretton, Arthur;
(2021)
Efficient Wasserstein Natural Gradients for Reinforcement Learning.
In:
ICLR 2021 - 9th International Conference on Learning Representations.
ICLR
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Abstract
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 improvements in both computational cost and performance over advanced baselines
| Type: | Proceedings paper |
|---|---|
| Title: | Efficient Wasserstein Natural Gradients for Reinforcement Learning |
| Event: | ICLR 2021 - 9th International Conference on Learning Representations |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://openreview.net/forum?id=OHgnfSrn2jv |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10168482 |
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