mlsanityData to 7 Oct 2026

Shielded Reinforcement Learning Under Dynamic Temporal Logic Constraints

arXiv 2603.17152 ↗First posted 17 Mar 2026cs.RO cs.LGcited by 1

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Abstract (arXiv)

Reinforcement Learning (RL) has shown promise in various robotics applications, yet its deployment on real systems is still limited due to safety and operational constraints. The safe RL field has gained considerable attention in recent years, which focuses on imposing safety constraints throughout the learning process. However, real systems often require more complex constraints than just safety, such as periodic recharging or time-bounded visits to specific regions. Imposing such spatio-temporal tasks during learning still remains a challenge. Signal Temporal Logic (STL) is a formal language for specifying temporal properties of real-valued signals and provides a way to express such complex tasks. In this paper, we propose a framework that leverages sequential control barrier functions and model-free RL to ensure that the given STL tasks are satisfied throughout the learning process. Our method extends beyond traditional safety constraints by enforcing rich STL specifications, which can involve visits to dynamic targets with unknown trajectories. We also demonstrate the effectiveness of our framework through various simulations.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 17 Mar 2026.

Builds on

15 of 23 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2017Constrained Policy Optimizationcited by 928
  5. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 261
  6. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
  7. 2018A Study on Overfitting in Deep Reinforcement Learningcited by 170
  8. 2016Q-Learning for Robust Satisfaction of Signal Temporal Logic Specificationscited by 65
  9. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 44
  10. 2019Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functionscited by 20
  11. 2014On-Line Monitoring for Temporal Logic Robustnesscited by 15
  12. 2021Probabilistically Guaranteed Satisfaction of Temporal Logic Constraints During Reinforcement Learningcited by 6
3 more
  1. 2022Control Barrier Functions with Actuation Constraints under Signal Temporal Logic Specificationscited by 2
  2. 2023Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windowscited by 2
  3. 2024Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switchingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligencecited by 0