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
2025-01-23
Patent
Electronic Resource
English
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
ArXiv | 2019
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