Reinforcement Learning For Online Testing Of Autonomous Driving Systems: A Replication And Extension Study

Abstract

In a recent study, Reinforcement Learning (RL) used in combination with many-objective search, has been shown to outperform alternative techniques (random search and many-objective search) for online testing of Deep Neural Network-enabled systems. The empirical evaluation of these techniques was conducted on a state-of-the-art Autonomous Driving System (ADS). This work is a replication and extension of that empirical study. Our replication shows that RL does not outperform pure random test generation in a comparison conducted under the same settings of the original study, but with no confounding factor coming from the way collisions are measured. Our extension aims at eliminating some of the possible reasons for the poor performance of RL observed in our replication: (1) the presence of reward components providing contrasting or useless feedback to the RL agent; (2) the usage of an RL algorithm (Q-learning) which requires discretization of an intrinsically continuous state space. Resul

Stats

• citations3
• S2 citations—
• github stars0
• HF likes0
• heat score4.52
• arxiv keygiamattei2024reinforcement

Related papers

• Reinforcement Learning For Testing Interdependent Requirements In Autonomous Vehicles: An Empirical Study (2026)0.00
• Towards Data-driven Offline Simulations For Online Reinforcement Learning (2022)0.00
• Accelerating Offline Reinforcement Learning Application In Real-time Bidding And Recommendation: Potential Use Of Simulation (2021)0.00
• AWAC: Accelerating Online Reinforcement Learning With Offline Datasets (2020)0.00
• Bridging The Gap Between Offline And Online Reinforcement Learning Evaluation Methodologies (2022)0.00
• Rethinking Out-of-distribution Detection For Reinforcement Learning: Advancing Methods For Evaluation And Detection (2024)2.26
• AD4RL: Autonomous Driving Benchmarks For Offline Reinforcement Learning With Value-based Dataset (2024)7.16
• An Optimistic Perspective On Offline Reinforcement Learning (2019)0.00