Documentdetail
ID kaart
oai:arXiv.org:2408.15150
Onderwerp
Computer Science - Software Engine...Auteur
Thomas, Deepak-George Biagiola, Matteo Humbatova, Nargiz Wardat, Mohammad Jahangirova, Gunel Rajan, Hridesh Tonella, PaoloCategorie
Computer Science
Jaar
2024
vermelding datum
04-09-2024
Trefwoorden
rl muprlBeschrijving
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid robot.
Correspondingly, novel approaches are needed to ensure that RL agents have been tested adequately before going to production.
Among them, mutation testing is quite promising, especially under the assumption that the injected faults (mutations) mimic the real ones.
In this paper, we first describe a taxonomy of real RL faults obtained by repository mining.
Then, we present the mutation operators derived from such real faults and implemented in the tool muPRL.
Finally, we discuss the experimental results, showing that muPRL is effective at discriminating strong from weak test generators, hence providing useful feedback to developers about the adequacy of the generated test scenarios.
;Comment: Accepted at ICSE '25
Thomas, Deepak-George,Biagiola, Matteo,Humbatova, Nargiz,Wardat, Mohammad,Jahangirova, Gunel,Rajan, Hridesh,Tonella, Paolo, 2024, muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain
sampling training learning vs
Comparison between Dual-Energy CT and Quantitative Susceptibility Mapping in Assessing Brain Iron Deposition in Parkinson Disease
nigra substantia healthy depositions p < 05 nucleus brain susceptibility ct bilateral dual-energy iron quantitative mapping values magnetic globus pallidus
