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Found 4 papers, 2 papers with code
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
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.
Boundary State Generation for Testing and Improvement of Autonomous Driving Systems
State-of-the-art ADS testing approaches modify the controllable attributes of a simulated driving environment until the ADS misbehaves.
Testing of Deep Reinforcement Learning Agents with Surrogate Models
The failure prediction acts as a fitness function, guiding the generation towards failure environment configurations, while saving computation time by deferring the execution of the DRL agent in the environment to those configurations that are more likely to expose failures.
Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing
Our empirical evaluation shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin.
