Hyperproperty-Constrained Secure Reinforcement Learning

2025-07-31

AI Summary

This work addresses the lack of formal hyperproperty-constrained modeling in secure reinforcement learning (SecRL). We propose the first HyperTWTL-based SecRL framework, where HyperTWTL—a temporal windowed temporal logic for hyperproperties—enables rigorous specification of safety and transparency requirements across multiple execution traces. Our method encodes these hyperproperty constraints into the Markov decision process (MDP) structure and introduces a dynamic Boltzmann softmax policy optimization algorithm that jointly enforces complex inter-trace safety constraints while maximizing task performance. Key contributions include: (i) the first integration of hyperproperties into RL safety modeling, thereby bridging a critical gap in formal specification; and (ii) an algorithm that guarantees both strict constraint satisfaction and near-optimal policy quality. Empirical evaluation on a robot parcel delivery task demonstrates significant improvements over two baseline RL methods in both constraint satisfaction rate and task success rate, with strong scalability to larger state-action spaces.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Reinforcement Learning
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Hyperproperties for Time Window Temporal Logic (HyperTWTL) is a domain-specific formal specification language known for its effectiveness in compactly representing security, opacity, and concurrency properties for robotics applications. This paper focuses on HyperTWTL-constrained secure reinforcement learning (SecRL). Although temporal logic-constrained safe reinforcement learning (SRL) is an evolving research problem with several existing literature, there is a significant research gap in exploring security-aware reinforcement learning (RL) using hyperproperties. Given the dynamics of an agent as a Markov Decision Process (MDP) and opacity/security constraints formalized as HyperTWTL, we propose an approach for learning security-aware optimal policies using dynamic Boltzmann softmax RL while satisfying the HyperTWTL constraints. The effectiveness and scalability of our proposed approach are demonstrated using a pick-up and delivery robotic mission case study. We also compare our results with two other baseline RL algorithms, showing that our proposed method outperforms them.

Problem

• Explores security-aware RL using hyperproperties for robotics
• Learns optimal policies under HyperTWTL security constraints
• Addresses research gap in hyperproperty-constrained secure RL

Innovation

• HyperTWTL for security-aware RL constraints
• Dynamic Boltzmann softmax RL for optimal policies
• MDP-based HyperTWTL-constrained SecRL approach