mlsanityData to 7 Oct 2026

Certified Reinforcement Learning with Logic Guidance

arXiv 1902.00778 ↗First posted 2 Feb 2019cs.LG stat.MLcited by 35

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Abstract (arXiv)

Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems. However, applications in safety-critical domains require a systematic and formal approach to specifying requirements as tasks or goals. We propose a model-free RL algorithm that enables the use of Linear Temporal Logic (LTL) to formulate a goal for unknown continuous-state/action Markov Decision Processes (MDPs). The given LTL property is translated into a Limit-Deterministic Generalised Buchi Automaton (LDGBA), which is then used to shape a synchronous reward function on-the-fly. Under certain assumptions, the algorithm is guaranteed to synthesise a control policy whose traces satisfy the LTL specification with maximal probability.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2019.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 8; 2024: 10; 2025: 5; 2026: 4.

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  1. 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systemscited by 94
  2. 2023Evaluating Cognitive Maps and Planning in Large Language Models with CogEvalcited by 77
  3. 2021Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logiccited by 38
  4. 2021Multi-Agent Reinforcement Learning with Temporal Logic Specificationscited by 27
  5. 2024DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RLcited by 20
  6. 2022Online Shielding for Reinforcement Learningcited by 19
  7. 2023ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learningcited by 15
  8. 2021Shielding Atari Games with Bounded Presciencecited by 14
  9. 2024Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programscited by 14
  10. 2024Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewardscited by 13
  11. 2022LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learningcited by 11
  12. 2022Correct-by-Construction Runtime Enforcement in AI -- A Surveycited by 10
23 more
  1. 2021Certification of Iterative Predictions in Bayesian Neural Networkscited by 8
  2. 2024Neural Model Checkingcited by 6
  3. 2023Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Surveycited by 5
  4. 2023Probabilistic Reach-Avoid for Bayesian Neural Networkscited by 4
  5. 2024On the Uniqueness of Solution for the Bellman Equation of LTL Objectivescited by 3
  6. 2024Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Explorationcited by 3
  7. 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Reviewcited by 3
  8. 2021Learning to Act Safely with Limited Exposure and Almost Sure Certaintycited by 2
  9. 2023System III: Learning with Domain Knowledge for Safety Constraintscited by 2
  10. 2026PlatoLTL: Scaling LTL-Guided Multi-Task RLcited by 2
  11. 2023Heterogeneous Knowledge for Augmented Modular Reinforcement Learningcited by 1
  12. 2023Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesiscited by 1
  13. 2024Regret-Free Reinforcement Learning for LTL Specificationscited by 1
  14. 2025SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RLcited by 1
  15. 2025Accelerated Learning with Linear Temporal Logic using Differentiable Simulationcited by 1
  16. 2023Topology-Guided Modular Actor-Critic Learning for Continuous Systems under Temporal Objectivescited by 0
  17. 2024Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learningcited by 0
  18. 2025Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectivescited by 0
  19. 2025A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processescited by 0
  20. 2025Physics-Informed Reward Machinescited by 0
  21. 2026Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning (Extended Version)cited by 0
  22. 2026Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specificationscited by 0
  23. 2026Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RLcited by 0