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Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving

arXiv 1904.07189 ↗First posted 15 Apr 2019cs.RO cs.AIcited by 28

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Abstract (arXiv)

Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain environments, but it does not provide any guarantee on the performance of the resulting policy. We propose a generic approach to enforce probabilistic guarantees on an RL agent. An exploration strategy is derived prior to training that constrains the agent to choose among actions that satisfy a desired probabilistic specification expressed with linear temporal logic (LTL). Reducing the search space to policies satisfying the LTL formula helps training and simplifies reward design. This paper outlines a case study of an intersection scenario involving multiple traffic participants. The resulting policy outperforms a rule-based heuristic approach in terms of efficiency while exhibiting strong guarantees on safety.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Apr 2019.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 2; 2024: 7; 2025: 3.

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  1. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  2. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  3. 2022Safe Reinforcement Learning via Shielding under Partial Observabilitycited by 56
  4. 2021Neural Network Repair with Reachability Analysiscited by 19
  5. 2021Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learningcited by 13
  6. 2022Dynamic Shielding for Reinforcement Learning in Black-Box Environmentscited by 11
  7. 2024Reward Machines for Deep RL in Noisy and Uncertain Environmentscited by 11
  8. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehiclescited by 10
  9. 2022Accelerated Reinforcement Learning for Temporal Logic Control Objectivescited by 9
  10. 2022Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Trafficcited by 8
  11. 2021Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarioscited by 7
  12. 2024Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutionscited by 7
16 more
  1. 2021Model-Free Learning of Safe yet Effective Controllerscited by 5
  2. 2022Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checkingcited by 5
  3. 2023Probabilistic Reach-Avoid for Bayesian Neural Networkscited by 5
  4. 2024Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Explorationcited by 5
  5. 2021Cooperation for Scalable Supervision of Autonomy in Mixed Trafficcited by 4
  6. 2024Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Drivingcited by 4
  7. 2021Learning Optimal Strategies for Temporal Tasks in Stochastic Gamescited by 3
  8. 2025Turn-based Multi-Agent Reinforcement Learning Model Checkingcited by 3
  9. 2021On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methodscited by 2
  10. 2024Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learningcited by 2
  11. 2025Compositional shield synthesis for safe reinforcement learning in partial observabilitycited by 2
  12. 2021Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Drivingcited by 1
  13. 2024Multi-agent transformer-accelerated RL for satisfaction of STL specificationscited by 1
  14. 2023Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Taskscited by 0
  15. 2024Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based Shieldingcited by 0
  16. 2025Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgridscited by 0