In various examples, a traffic model including one or more traffic scenarios may be generated and/or updated based on using human feedback. Human feedback may be provided indicating a preference for various traffic scenarios to identify which scenarios in a model are more realistic. A reward model may capture the preference information and rank the realism of one or more traffic scenarios.
REINFORCEMENT LEARNING FOR TRAFFIC SIMULATION
23.01.2025
Patent
Elektronische Ressource
Englisch
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
ArXiv | 2019
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