On the Limits of Digital Twins for Safe Deep Reinforcement Learning in Robotic Networks
Author:
Affiliation:
1. University of Carthage,SUP'COM,Tunisia
2. University of Padova,Dept. of Information Engineering,Italy
3. University of Modena and Reggio Emilia,Dept. of Physics, Mathematics and Informatics,Italy
Funder
European Union
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10620660/10620661/10620857.pdf?arnumber=10620857
Reference19 articles.
1. A Collaborative Internet of Things Architecture for Smart Cities and Environmental Monitoring
2. Transferring policy of deep reinforcement learning from simulation to reality for robotics
3. Explainable Reinforcement Learning through a Causal Lens
4. Generation of Policy-Level Explanations for Reinforcement Learning
5. Explainable Deep Reinforcement Learning: State of the Art and Challenges
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