Performative Policy Gradient: Optimality in Performative Reinforcement Learning

• URL: http://arxiv.org/abs/2512.20576v1
• Date: Tue, 23 Dec 2025 18:20:06 GMT
• Title: Performative Policy Gradient: Optimality in Performative Reinforcement Learning
• Authors: Debabrota Basu, Udvas Das, Brahim Driss, Uddalak Mukherjee,
• Abstract summary: Post-deployment machine learning algorithms often influence the environments they act in.<n>We introduce the Performative Policy Gradient algorithm (PePG)<n>PePG converges to performatively optimal policies, i.e. policies that remain optimal under the distribution shifts induced by themselves.
• Score: 13.777823115521665
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Post-deployment machine learning algorithms often influence the environments they act in, and thus shift the underlying dynamics that the standard reinforcement learning (RL) methods ignore. While designing optimal algorithms in this performative setting has recently been studied in supervised learning, the RL counterpart remains under-explored. In this paper, we prove the performative counterparts of the performance difference lemma and the policy gradient theorem in RL, and further introduce the Performative Policy Gradient algorithm (PePG). PePG is the first policy gradient algorithm designed to account for performativity in RL. Under softmax parametrisation, and also with and without entropy regularisation, we prove that PePG converges to performatively optimal policies, i.e. policies that remain optimal under the distribution shifts induced by themselves. Thus, PePG significantly extends the prior works in Performative RL that achieves performative stability but not optimality. Furthermore, our empirical analysis on standard performative RL environments validate that PePG outperforms standard policy gradient algorithms and the existing performative RL algorithms aiming for stability.

Related papers

• Stabilizing Policy Optimization via Logits Convexity [59.242732612484474]
  We show that the convexity of the supervised fine-tuning loss with respect to model logits plays a key role in enabling stable training.<n>Motivated by this observation, we propose Logits Convex Optimization (LCO), a simple yet effective policy optimization framework.
  arXiv  Detail & Related papers  (2026-03-01T07:40:12Z)
• Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation [10.35045003737115]
  Decision-making under distribution shift is a central challenge in reinforcement learning (RL), where training and deployment environments differ.<n>We propose DR-RPO, a model-free online policy optimization method that learns robust policies with sublinear regret.<n>We show that DR-RPO can achieve suboptimality bounds and sample efficiency in robust RL, matching the performance of value-based approaches.
  arXiv  Detail & Related papers  (2025-10-16T02:56:58Z)
• On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning [59.11784194183928]
  Policy gradient algorithms have been successfully applied to enhance the reasoning capabilities of large language models (LLMs)<n>Regularized Policy Gradient (RPG) view shows that the widely-used $k_3$ penalty is exactly the unnormalized KL.<n>RPG-REINFORCE with RPG-Style Clip improves accuracy by up to $+6$ absolute percentage points over DAPO.
  arXiv  Detail & Related papers  (2025-05-23T06:01:21Z)
• On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning [30.767979998925437]
  Reinforcement learning with general utilities (RLGU) offers a unifying framework to capture problems beyond standard expected returns.<n>Recent advances in theoretical analysis of policy gradient (PG) methods for standard RL and recent efforts in RLGU still remain limited.<n>We establish global optimality guarantees of PG methods for RLGU in which the objective is a general concave utility function of the state-action occupancy measure.
  arXiv  Detail & Related papers  (2024-10-05T10:24:07Z)
• A Prospect-Theoretic Policy Gradient Framework for Behaviorally Nuanced Reinforcement Learning [4.841365627573421]
  Cumulative Prospect Theory (CPT) provides a more nuanced model for human-based decision-making.<n>CPT provides a more nuanced model for human-based decision-making, capturing diverse attitudes and perceptions toward risk, gains, and losses.<n>Our contributions are as follows: (a) we derive a novel policy gradient theorem for CPT objectives, (b) we design a model-free policy gradient algorithm for solving the CPT-RL problem, and (d) test its performance through simulations.
  arXiv  Detail & Related papers  (2024-10-03T15:45:39Z)
• Learning Optimal Deterministic Policies with Stochastic Policy Gradients [62.81324245896716]
  Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. In common practice, convergence (hyper)policies are learned only to deploy their deterministic version. We show how to tune the exploration level used for learning to optimize the trade-off between the sample complexity and the performance of the deployed deterministic policy.
  arXiv  Detail & Related papers  (2024-05-03T16:45:15Z)
• Actor-Critic Reinforcement Learning with Phased Actor [10.577516871906816]
  We propose a novel phased actor in actor-critic (PAAC) method to improve policy gradient estimation. PAAC accounts for both $Q$ value and TD error in its actor update. Results show that PAAC leads to significant performance improvement measured by total cost, learning variance, robustness, learning speed and success rate.
  arXiv  Detail & Related papers  (2024-04-18T01:27:31Z)
• Iteratively Refined Behavior Regularization for Offline Reinforcement Learning [57.10922880400715]
  In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. By iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks.
  arXiv  Detail & Related papers  (2023-06-09T07:46:24Z)
• Offline Policy Optimization in RL with Variance Regularizaton [142.87345258222942]
  We propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms.
  arXiv  Detail & Related papers  (2022-12-29T18:25:01Z)
• OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation [59.469401906712555]
  We present an offline reinforcement learning algorithm that prevents overestimation in a more principled way. Our algorithm, OptiDICE, directly estimates the stationary distribution corrections of the optimal policy. We show that OptiDICE performs competitively with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-21T00:43:30Z)
• PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning [35.044047991893365]
  This work introduces the the Policy Cover-Policy Gradient (PC-PG) algorithm, which balances the exploration vs. exploitation tradeoff using an ensemble of policies (the policy cover) We show that PC-PG has strong guarantees under model misspecification that go beyond the standard worst case $ell_infty$ assumptions. We also complement the theory with empirical evaluation across a variety of domains in both reward-free and reward-driven settings.
  arXiv  Detail & Related papers  (2020-07-16T16:57:41Z)
• Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization [44.24881971917951]
  Natural policy gradient (NPG) methods are among the most widely used policy optimization algorithms. We develop convergence guarantees for entropy-regularized NPG methods under softmax parameterization. Our results accommodate a wide range of learning rates, and shed light upon the role of entropy regularization in enabling fast convergence.
  arXiv  Detail & Related papers  (2020-07-13T17:58:41Z)
• Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO [90.90009491366273]
  We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms. Specifically, we investigate the consequences of "code-level optimizations:" Our results show that they (a) are responsible for most of PPO's gain in cumulative reward over TRPO, and (b) fundamentally change how RL methods function.
  arXiv  Detail & Related papers  (2020-05-25T16:24:59Z)

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.