A runtime monitoring framework to enforce invariants on reinforcement learning agents exploring complex environments
Paper i proceeding, 2019
Reinforcement learning
LTL invariants
Reward shaping
Runtime monitoring
Författare
Piergiuseppe Mallozzi
Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for People, Architecture, Requirements and Traceability
Ezequiel Castellano
The Graduate University for Advanced Studies (SOKENDAI)
Patrizio Pelliccione
Universita degli Studi dell'Aquila
Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems
Gerardo Schneider
Chalmers, Data- och informationsteknik, Formella metoder
Kenji Tei
Waseda University
Proceedings - 2019 IEEE/ACM 2nd International Workshop on Robotics Software Engineering, RoSE 2019
5-12 8823721
Montreal, Canada,
Ämneskategorier
Lärande
Robotteknik och automation
Datavetenskap (datalogi)
DOI
10.1109/RoSE.2019.00011