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Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions

arXiv 1903.09885 ↗First posted 23 Mar 2019cs.LG stat.MLcited by 21

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Abstract (arXiv)

Using reinforcement learning to learn control policies is a challenge when the task is complex with potentially long horizons. Ensuring adequate but safe exploration is also crucial for controlling physical systems. In this paper, we use temporal logic to facilitate specification and learning of complex tasks. We combine temporal logic with control Lyapunov functions to improve exploration. We incorporate control barrier functions to safeguard the exploration and deployment process. We develop a flexible and learnable system that allows users to specify task objectives and constraints in different forms and at various levels. The framework is also able to take advantage of known system dynamics and handle unknown environmental dynamics by integrating model-free learning with model-based planning.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Mar 2019.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 6; 2024: 4; 2025: 1; 2026: 3.

21 papers.

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  1. 2022Constrained Update Projection Approach to Safe Policy Optimizationcited by 58
  2. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 36
  3. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 24
  4. 2024Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulationcited by 21
  5. 2023A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviorscited by 15
  6. 2024Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learningcited by 9
  7. 2021Reactive and Safe Road User Simulations using Neural Barrier Certificatescited by 7
  8. 2021Provably Correct Training of Neural Network Controllers Using Reachability Analysiscited by 5
  9. 2021Model-Free Learning of Safe yet Effective Controllerscited by 5
  10. 2025A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functionscited by 4
  11. 2021Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contextscited by 3
  12. 2023Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretationcited by 3
9 more
  1. 2023ConBaT: Control Barrier Transformer for Safe Policy Learningcited by 3
  2. 2023Learning Adaptive Safety for Multi-Agent Systemscited by 3
  3. 2024On the Uniqueness of Solution for the Bellman Equation of LTL Objectivescited by 3
  4. 2023Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesiscited by 1
  5. 2026Shielded Reinforcement Learning Under Dynamic Temporal Logic Constraintscited by 1
  6. 2021Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafecited by 0
  7. 2024Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learningcited by 0
  8. 2026Integrating LTL Constraints into PPO for Safe Reinforcement Learningcited by 0
  9. 2026Kernel-Based Safe Exploration in Deep Reinforcement Learningcited by 0