Policy Optimization as Wasserstein Gradient Flows

9 August 2018

Lawrence Carin

Papers citing "Policy Optimization as Wasserstein Gradient Flows"

44 / 44 papers shown

Title
Wasserstein Policy Optimization David Pfau Ian Davies Diana Borsa Joao G. M. Araujo Brendan D. Tracey H. V. Hasselt 29 0 0 01 May 2025
Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions Shang Wu Yazhen Wang 43 0 0 11 Jan 2025
Linear convergence of proximal descent schemes on the Wasserstein space Razvan-Andrei Lascu Mateusz B. Majka David Siska Łukasz Szpruch 72 1 0 22 Nov 2024
Optimal Transport-Assisted Risk-Sensitive Q-Learning Zahra Shahrooei Ali Baheri 27 2 0 17 Jun 2024
Wasserstein gradient flow for optimal probability measure decomposition Jiangze Han Chris Ryan Xin T. Tong 21 1 0 03 Jun 2024
On the Benefit of Optimal Transport for Curriculum Reinforcement Learning Pascal Klink Carlo DÉramo Jan Peters J. Pajarinen 30 3 0 25 Sep 2023
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning Haorui Li Jiaqi Liang Linjing Li D. Zeng 9 0 0 02 Aug 2023
Recent Advances in Optimal Transport for Machine Learning Eduardo Fernandes Montesuma Fred-Maurice Ngole-Mboula Antoine Souloumiac OOD OT 15 31 0 28 Jun 2023
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods Jun Song Niao He Lijun Ding Chaoyue Zhao 25 3 0 25 Jun 2023
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies Hanna Ziesche Leonel Rozo 24 5 0 17 May 2023
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates Louis Sharrock Christopher Nemeth BDL 15 8 0 26 Jan 2023
Proximal Mean Field Learning in Shallow Neural Networks Alexis M. H. Teter Iman Nodozi A. Halder FedML 35 1 0 25 Oct 2022
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions Antonio Terpin Nicolas Lanzetti Batuhan Yardim Florian Dorfler Giorgia Ramponi 11 5 0 20 Oct 2022
Batch Bayesian Optimization via Particle Gradient Flows Enrico Crovini S. Cotter K. Zygalakis Andrew B. Duncan 11 3 0 10 Sep 2022
Information-Theoretic Equivalence of Entropic Multi-Marginal Optimal Transport: A Theory for Multi-Agent Communication Shuchan Wang OT 20 0 0 22 Aug 2022
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning Harley Wiltzer D. Meger Marc G. Bellemare 14 12 0 24 May 2022
A Distributed Algorithm for Measure-valued Optimization with Additive Objective Iman Nodozi A. Halder 15 1 0 17 Feb 2022
Wasserstein Unsupervised Reinforcement Learning Shuncheng He Yuhang Jiang Hongchang Zhang Jianzhun Shao Xiangyang Ji OffRL 17 22 0 15 Oct 2021
Generalized Talagrand Inequality for Sinkhorn Distance using Entropy Power Inequality Shuchan Wang Photios A. Stavrou Mikael Skoglund 14 4 0 17 Sep 2021
Sqrt(d) Dimension Dependence of Langevin Monte Carlo Ruilin Li H. Zha Molei Tao 11 30 0 08 Sep 2021
Large-Scale Wasserstein Gradient Flows Petr Mokrov Alexander Korotin Lingxiao Li Aude Genevay Justin Solomon Evgeny Burnaev 22 71 0 01 Jun 2021
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport A. Givchi Pei Wang Junqi Wang Patrick Shafto OT OffRL 11 0 0 15 Feb 2021
A contribution to Optimal Transport on incomparable spaces Titouan Vayer OT 17 19 0 09 Nov 2020
Hessian-Free High-Resolution Nesterov Acceleration for Sampling Ruilin Li H. Zha Molei Tao 12 6 0 16 Jun 2020
Improving Adversarial Text Generation by Modeling the Distant Future Ruiyi Zhang Changyou Chen Zhe Gan Wenlin Wang Dinghan Shen Guoyin Wang Zheng Wen Lawrence Carin 22 12 0 04 May 2020
Regularizing activations in neural networks via distribution matching with the Wasserstein metric Taejong Joo Donggu Kang Byunghoon Kim 27 8 0 13 Feb 2020
Nested-Wasserstein Self-Imitation Learning for Sequence Generation Ruiyi Zhang Changyou Chen Zhe Gan Zheng Wen Wenlin Wang Lawrence Carin 23 5 0 20 Jan 2020
Straight-Through Estimator as Projected Wasserstein Gradient Flow Pengyu Cheng YooJung Choi Yitao Liang Dinghan Shen Ricardo Henao Guy Van den Broeck 22 14 0 05 Oct 2019
Accelerated Information Gradient flow Yifei Wang Wuchen Li 15 55 0 04 Sep 2019
Learning Policies through Quantile Regression Oliver Richter Roger Wattenhofer 11 0 0 27 Jun 2019
Learning to Score Behaviors for Guided Policy Optimization Aldo Pacchiano Jack Parker-Holder Yunhao Tang A. Choromańska K. Choromanski Michael I. Jordan 11 38 0 11 Jun 2019
Stochastically Dominant Distributional Reinforcement Learning John D. Martin Michal Lyskawinski Xiaohu Li Brendan Englot 15 24 0 17 May 2019
Stein Point Markov Chain Monte Carlo W. Chen Alessandro Barp François‐Xavier Briol Jackson Gorham Mark Girolami Lester W. Mackey Chris J. Oates 30 56 0 09 May 2019
Orthogonal Estimation of Wasserstein Distances Mark Rowland Jiri Hron Yunhao Tang K. Choromanski Tamás Sarlós Adrian Weller 31 43 0 09 Mar 2019
Scalable Thompson Sampling via Optimal Transport Ruiyi Zhang Zheng Wen Changyou Chen Lawrence Carin OT 8 20 0 19 Feb 2019
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning Casey Chu Jose H. Blanchet Peter Glynn GAN 9 26 0 30 Jan 2019
Improving Sequence-to-Sequence Learning via Optimal Transport Liqun Chen Yizhe Zhang Ruiyi Zhang Chenyang Tao Zhe Gan Haichao Zhang Bai Li Dinghan Shen Changyou Chen Lawrence Carin OT 6 92 0 18 Jan 2019
Accelerated Flow for Probability Distributions Amirhossein Taghvaei P. Mehta 23 30 0 10 Jan 2019
Variance Reduction in Stochastic Particle-Optimization Sampling Jianyi Zhang Yang Zhao Changyou Chen OT 11 13 0 20 Nov 2018
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties David Tellez G. Litjens J. A. van der Laak R. Tavenard F. Ciompi OT 15 121 0 07 Nov 2018
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory Jianyi Zhang Ruiyi Zhang Lawrence Carin Changyou Chen 10 46 0 05 Sep 2018
Understanding and Accelerating Particle-Based Variational Inference Chang-rui Liu Jingwei Zhuo Pengyu Cheng Ruiyi Zhang Jun Zhu Lawrence Carin 9 14 0 04 Jul 2018
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling Changyou Chen Ruiyi Zhang Wenlin Wang Bai Li Liqun Chen 13 86 0 29 May 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 279 9,136 0 06 Jun 2015