[34] Yongxin Chen, Tryphon T. Georgiou, and Allen Tannenbaum. Optimal transport for Gaussian mixture models. IEEE Access, 7:6269-6278, 2019. URL https://doi.org/10.1109/ACCESS.2018.2889838.2, 4, 6
[35] Julie Delon and Agnès Desolneux. A Wasserstein-type distance in the space of Gaussian mixture models. SIAM Journal on Imaging Sciences, 13(2):936-970, 2020. URL https://doi.org/10.1137/19M1301047.2, 4, 6
[36] Pierre H. Richemond and Brendan Maginnis. On Wasserstein reinforcement learning and the Fokker-Planck equation, 2017. URL https://arxiv.org/abs/1712.07185. arXiv:1712.07185.2, 3, 4, 6
[37] Richard Jordan, David Kinderlehrer, and Felix Otto. The variational formulation of the Fokker-Planck equation. Siam Journal on Applied Mathematics, 1996. URL https://doi.org/10.1137/S0036141096303359.2, 4, 6
[38] Ruiyi Zhang, Changyou Chen, Chunyuan Li, and Lawrence Carin. Policy optimization as Wasserstein gradient flows. In International Conference on Machine Learning (ICML), pages 5737-5746, 2018. URL https://proceedings.mlr.press/v80/zhang18a.html.2,3
[39] Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin Solomon, and Evgeny Burnaev. Large-scale Wasserstein gradient flows. In Neural Information Processing Systems (NeurIPS), 2021. URL https://openreview.net/forum?id=nLjIuHsMHp.2
[40] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International Conference on Machine Learning (ICML), pages 1861-1870, 2018. URL https://proceedings.mlr.press/v80/haarnoja18b.html.3
[41] Jie Ren, Yewen Li, Zihan Ding, Wei Pan, and Hao Dong. Probabilistic mixture-of-experts for efficient deep reinforcement learning, 2021. URL https://arxiv.org/abs/2104.09122.3, 8, 9
[42] Shijun Wang, Baocheng Zhu, Chen Li, Mingzhe Wu, James Zhang, Wei Chu, and Yuan Qi. Riemannian proximal policy optimization, 2020. URL https://arxiv.org/abs/2005.09195. arXiv:2005.09195.3
[43] Shun-ichi Amari. Natural gradient works efficiently in learning. Neural Computation, 10(2):251–276, 1998. URL https://doi.org/10.1162/089976698300017746.3
[44] Sham M Kakade. A natural policy gradient. In Neural Information Processing Systems (NeurIPS), 2001. URL https://proceedings.neurips.cc/paper_files/paper/2001/file/4b86abe48d358ecf194c56c69108433e-Paper.pdf. 3
[45] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms, 2017. URL https://arxiv.org/abs/1707.06347. 3, 5, 8
[46] Alexandre Galashov, Siddhant Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojtek M. Czarnecki, Yee Whye Teh, Razvan Pascanu, and Nicolas Heess. Information asymmetry in KL-regularized RL. In International Conference on Learning Representations (ICLR), 2019. URL https://openreview.net/forum?id=S11qMn05Ym.3
[47] Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, and Michael Jordan. Learning to score behaviors for guided policy optimization. In International Conference on Machine Learning (ICML), pages 7445-7454, 2020. URL https://proceedings.mlr.press/v119/pacchiano20a.html.3
[48] Ted Moskovitz, Michael Arbel, Ferenc Huszar, and Arthur Gretton. Efficient Wasserstein natural gradients for reinforcement learning. In International Conference on Learning Representations (ICLR), 2021. URL https://openreview.net/forum?id=0HgnfSrn2jv.3
[49] Wu Lin, Mark Schmidt, and Mohammad Emtiyaz Khan. Handling the positive-definite constraint in the Bayesian learning rule. In International Conference on Machine Learning (ICML), pages 6116-6126, 2020. URL https://proceedings.mlr.press/v119/lin20d.html.3
[50] Filippo Santambrogio. Euclidean, metric, and Wasserstein gradient flows: an overview. Bull. Math. Sci., 7: 87-154, 2017. URL https://doi.org/10.1007/s13373-017-0101-1.3
[51] Luigi Ambrosio, Nicola Gigli, and Giuseppe Savare. Gradient flows: in metric spaces and in the space of probability measures. Springer Science & Business Media, 2005. URL https://doi.org/10.1007/b137080.4,6