Vulnerability Analysis of Safe Reinforcement Learning via Inverse Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2602.16543v1
• Date: Wed, 18 Feb 2026 15:43:36 GMT
• Title: Vulnerability Analysis of Safe Reinforcement Learning via Inverse Constrained Reinforcement Learning
• Authors: Jialiang Fan, Shixiong Jiang, Mengyu Liu, Fanxin Kong,
• Abstract summary: We propose an adversarial attack framework to reveal vulnerabilities of Safe RL policies.<n>Our framework learns a constraint model and a surrogate (learner) policy, enabling gradient-based attack optimization.
• Score: 1.4707788677208018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe reinforcement learning (Safe RL) aims to ensure policy performance while satisfying safety constraints. However, most existing Safe RL methods assume benign environments, making them vulnerable to adversarial perturbations commonly encountered in real-world settings. In addition, existing gradient-based adversarial attacks typically require access to the policy's gradient information, which is often impractical in real-world scenarios. To address these challenges, we propose an adversarial attack framework to reveal vulnerabilities of Safe RL policies. Using expert demonstrations and black-box environment interaction, our framework learns a constraint model and a surrogate (learner) policy, enabling gradient-based attack optimization without requiring the victim policy's internal gradients or the ground-truth safety constraints. We further provide theoretical analysis establishing feasibility and deriving perturbation bounds. Experiments on multiple Safe RL benchmarks demonstrate the effectiveness of our approach under limited privileged access.

Related papers

• Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models [57.006252510102506]
  Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications.<n>We introduce a novel recovery-based shielding framework that enables safe RL with a provable safety lower bound for unknown and non-linear continuous dynamical systems.
  arXiv  Detail & Related papers  (2026-02-12T22:03:35Z)
• Safe Exploration via Policy Priors [45.58021831092113]
  We show that SOOPER guarantees safety throughout learning, and establish convergence to an optimal policy by bounding its cumulative regret.<n>Experiments on key safe RL benchmarks and real-world hardware demonstrate that SOOPER is scalable, outperforms the state-of-the-art and validate our theoretical guarantees in practice.
  arXiv  Detail & Related papers  (2026-01-27T13:45:28Z)
• Safety Modulation: Enhancing Safety in Reinforcement Learning through Cost-Modulated Rewards [23.15178050525514]
  Safe Reinforcement Learning (Safe RL) aims to train an RL agent to maximize its performance in real-world environments while adhering to safety constraints.<n>We propose a novel safe RL approach called Safety Modulated Policy Optimization (SMPO), which enables safe policy function learning.
  arXiv  Detail & Related papers  (2025-04-03T21:35:22Z)
• Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning [7.888219789657414]
  In safe offline reinforcement learning (RL), the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints.<n>We address these issues with a novel approach that begins by learning a conservatively safe policy through the use of Conditional Variational Autoencoders.<n>We frame this as a Constrained Reward-Return Maximization problem, wherein the policy aims to optimize rewards while complying with the inferred latent safety constraints.
  arXiv  Detail & Related papers  (2024-12-11T22:00:07Z)
• Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning [4.14360329494344]
  Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the crucial challenge of ensuring safety. Traditional safe RL approaches have predominantly focused on incorporating predefined safety constraints into the policy learning process. We propose a novel approach that concurrently learns a safe RL control policy and identifies the unknown safety constraint parameters of a given environment.
  arXiv  Detail & Related papers  (2024-02-24T20:01:15Z)
• A Survey of Constraint Formulations in Safe Reinforcement Learning [15.593999581562203]
  Safety is critical when applying reinforcement learning to real-world problems. A prevalent safe RL approach is based on a constrained criterion, which seeks to maximize the expected cumulative reward. Despite recent effort to enhance safety in RL, a systematic understanding of the field remains difficult.
  arXiv  Detail & Related papers  (2024-02-03T04:40:31Z)
• Safe Reinforcement Learning via Confidence-Based Filters [78.39359694273575]
  We develop a control-theoretic approach for certifying state safety constraints for nominal policies learned via standard reinforcement learning techniques. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-07-04T11:43:23Z)
• SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition [59.94644674087599]
  We propose SAFEty skill pRiors (SAFER), an algorithm that accelerates policy learning on complex control tasks under safety constraints. Through principled training on an offline dataset, SAFER learns to extract safe primitive skills. In the inference stage, policies trained with SAFER learn to compose safe skills into successful policies.
  arXiv  Detail & Related papers  (2022-02-10T05:43:41Z)
• Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations [64.39401322671803]
  This paper explores the possibility of safe RL algorithms with zero training-time safety violations. We propose an algorithm, Co-trained Barrier Certificate for Safe RL (CRABS), which iteratively learns barrier certificates, dynamics models, and policies.
  arXiv  Detail & Related papers  (2021-08-04T04:59:05Z)
• Conservative Safety Critics for Exploration [120.73241848565449]
  We study the problem of safe exploration in reinforcement learning (RL) We learn a conservative safety estimate of environment states through a critic. We show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates.
  arXiv  Detail & Related papers  (2020-10-27T17:54:25Z)
• Cautious Reinforcement Learning with Logical Constraints [78.96597639789279]
  An adaptive safe padding forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring safety during the learning process. Theoretical guarantees are available on the optimality of the synthesised policies and on the convergence of the learning algorithm.
  arXiv  Detail & Related papers  (2020-02-26T00:01:08Z)

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.