Reinforcement learning for online testing of autonomous driving systems : a replication and extension study
Additional information
Authors
Giamattei L.,
Biagiola M.,
Pietrantuono R.,
Russo S.,
Tonella P.
Type
Journal Article
Year
2024
Language
English
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 feedback to the RL agent; (2) the usage of an RL algorithm (Q-learning) which requires discretization of an intrinsically continuous state space. Results show that our new RL agent is able to converge to an effective policy that outperforms random search. Results also highlight other possible improvements, which open to further investigations on how to best leverage RL for online ADS testing.
Keywords
Reinforcement learning, Autonomous driving systems, Online testing, Replication study, Extension study
Journal
Empirical Software Engineering
Volume
30
Number ( Month )
1
Pages (or article number)
19
ISSN
1382-3256, 1573-7616
Diffusion
License
CC BY-NC-ND
Visibility
Public
Status open access
Hybrid
