# Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving

arXiv: https://arxiv.org/abs/1904.07189 · HTML: https://mlsanity.com/paper/1904.07189/

## Key facts

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

## Built on by (most cited first, 28 of 28)

- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Safe Reinforcement Learning via Shielding under Partial Observability](https://mlsanity.com/paper/2204.00755/) (2022)
- [Neural Network Repair with Reachability Analysis](https://mlsanity.com/paper/2108.04214/) (2021)
- [Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning](https://mlsanity.com/paper/2102.03119/) (2021)
- [Dynamic Shielding for Reinforcement Learning in Black-Box Environments](https://mlsanity.com/paper/2207.13446/) (2022)
- [Reward Machines for Deep RL in Noisy and Uncertain Environments](https://mlsanity.com/paper/2406.00120/) (2024)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Accelerated Reinforcement Learning for Temporal Logic Control Objectives](https://mlsanity.com/paper/2205.04424/) (2022)
- [Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic](https://mlsanity.com/paper/2202.09644/) (2022)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Model-Free Learning of Safe yet Effective Controllers](https://mlsanity.com/paper/2103.14600/) (2021)
- [Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking](https://mlsanity.com/paper/2212.05337/) (2022)
- [Probabilistic Reach-Avoid for Bayesian Neural Networks](https://mlsanity.com/paper/2310.01951/) (2023)
- [Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration](https://mlsanity.com/paper/2410.12136/) (2024)
- [Cooperation for Scalable Supervision of Autonomy in Mixed Traffic](https://mlsanity.com/paper/2112.07569/) (2021)
- [Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2403.18209/) (2024)
- [Learning Optimal Strategies for Temporal Tasks in Stochastic Games](https://mlsanity.com/paper/2102.04307/) (2021)
- [Turn-based Multi-Agent Reinforcement Learning Model Checking](https://mlsanity.com/paper/2501.03187/) (2025)
- [On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods](https://mlsanity.com/paper/2111.04865/) (2021)
- [Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.13481/) (2024)
- [Compositional shield synthesis for safe reinforcement learning in partial observability](https://mlsanity.com/paper/2509.12085/) (2025)
- [Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving](https://mlsanity.com/paper/2102.03053/) (2021)
- [Multi-agent transformer-accelerated RL for satisfaction of STL specifications](https://mlsanity.com/paper/2403.15916/) (2024)
- [Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks](https://mlsanity.com/paper/2311.10863/) (2023)
- [Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based Shielding](https://mlsanity.com/paper/2407.04343/) (2024)
- [Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids](https://mlsanity.com/paper/2512.01046/) (2025)

## 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.