Wasserstein Proximal Policy Gradient

• URL: http://arxiv.org/abs/2603.02576v1
• Date: Tue, 03 Mar 2026 03:48:09 GMT
• Title: Wasserstein Proximal Policy Gradient
• Authors: Zhaoyu Zhu, Shuhan Zhang, Rui Gao, Shuang Li,
• Abstract summary: We study policy gradient methods for continuous-action, entropy-regularized learning through the lens of Wasserstein geometry.<n>We derive Wasserstein Proximal Policy Gradient (WWPG) via an operator-splitting scheme that alternates an optimal transport update with a heat step implemented by WPPG.<n>We establish a global linear convergence rate for WPPG, covering both exact policy evaluation and actor-critic implementations with controlled approximation error.
• Score: 10.574676421687718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study policy gradient methods for continuous-action, entropy-regularized reinforcement learning through the lens of Wasserstein geometry. Starting from a Wasserstein proximal update, we derive Wasserstein Proximal Policy Gradient (WPPG) via an operator-splitting scheme that alternates an optimal transport update with a heat step implemented by Gaussian convolution. This formulation avoids evaluating the policy's log density or its gradient, making the method directly applicable to expressive implicit stochastic policies specified as pushforward maps. We establish a global linear convergence rate for WPPG, covering both exact policy evaluation and actor-critic implementations with controlled approximation error. Empirically, WPPG is simple to implement and attains competitive performance on standard continuous-control benchmarks.

Related papers

• Achieve Performatively Optimal Policy for Performative Reinforcement Learning [55.983627302691424]
  This work proposes a zeroth-order FrankWolfe- (0FW) algorithm with a gradient of performative policy in the framework.<n> Experimental results demonstrate that our 0FW is more effective than the existing approximation in finding the desired PO policy.
  arXiv  Detail & Related papers  (2025-10-06T01:56:31Z)
• Reusing Trajectories in Policy Gradients Enables Fast Convergence [59.27926064817273]
  Policy gradient (PG) methods are a class of effective reinforcement learning algorithms.<n>We propose RPG (Retrospective Policy Gradient), a PG algorithm that combines old and new trajectories for policy updates.<n>Under established assumptions, RPG achieves a sample complexity of $widetildeO(epsilon-1)$, the best known rate in the literature.
  arXiv  Detail & Related papers  (2025-06-06T15:42:15Z)
• Wasserstein Policy Optimization [15.269409777313662]
  Wasserstein Policy Optimization (WPO) is an actor-critic algorithm for reinforcement learning in continuous action spaces.<n>We show results on the DeepMind Control Suite and a magnetic confinement task which compare favorably with state-of-the-art continuous control methods.
  arXiv  Detail & Related papers  (2025-05-01T17:07:01Z)
• Deterministic Policy Gradient Primal-Dual Methods for Continuous-Space Constrained MDPs [82.34567890576423]
  We develop a deterministic policy gradient primal-dual method to find an optimal deterministic policy with non-asymptotic convergence.<n>We prove that the primal-dual iterates of D-PGPD converge at a sub-linear rate to an optimal regularized primal-dual pair.<n>This appears to be the first work that proposes a deterministic policy search method for continuous-space constrained MDPs.
  arXiv  Detail & Related papers  (2024-08-19T14:11:04Z)
• Gradient Informed Proximal Policy Optimization [35.22712034665224]
  We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm. By adaptively modifying the alpha value, we can effectively manage the influence of analytical policy gradients during learning. Our proposed approach outperforms baseline algorithms in various scenarios, such as function optimization, physics simulations, and traffic control environments.
  arXiv  Detail & Related papers  (2023-12-14T07:50:21Z)
• Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs [107.28031292946774]
  We study the problem of computing an optimal policy of an infinite-horizon discounted Markov decision process (constrained MDP) We develop two single-time-scale policy-based primal-dual algorithms with non-asymptotic convergence of their policy iterates to an optimal constrained policy. To the best of our knowledge, this work appears to be the first non-asymptotic policy last-iterate convergence result for single-time-scale algorithms in constrained MDPs.
  arXiv  Detail & Related papers  (2023-06-20T17:27:31Z)
• Truly Deterministic Policy Optimization [3.07015565161719]
  We present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape. We show that it is possible to compute exact advantage estimates if both the state transition model and the policy are deterministic.
  arXiv  Detail & Related papers  (2022-05-30T18:49:33Z)
• Deep Bayesian Quadrature Policy Optimization [100.81242753620597]
  Deep Bayesian quadrature policy gradient (DBQPG) is a high-dimensional generalization of Bayesian quadrature for policy gradient estimation. We show that DBQPG can substitute Monte-Carlo estimation in policy gradient methods, and demonstrate its effectiveness on a set of continuous control benchmarks.
  arXiv  Detail & Related papers  (2020-06-28T15:44:47Z)
• Zeroth-order Deterministic Policy Gradient [116.87117204825105]
  We introduce Zeroth-order Deterministic Policy Gradient (ZDPG) ZDPG approximates policy-reward gradients via two-point evaluations of the $Q$function. New finite sample complexity bounds for ZDPG improve upon existing results by up to two orders of magnitude.
  arXiv  Detail & Related papers  (2020-06-12T16:52:29Z)
• Stochastic Recursive Momentum for Policy Gradient Methods [28.277961340108313]
  We propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (Storm-PG) Storm-PG enjoys a provably sharp $O (1/epsilon3)$ sample bound for STORM-PG, matching the best-known convergence rate for policy gradient algorithm. Numerical experiments depicts the superiority of our algorithm over comparative policy gradient algorithms.
  arXiv  Detail & Related papers  (2020-03-09T17:59:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.