A Comparative Study of Deep Reinforcement Learning Algorithms for Urban Autonomous Driving: Addressing the Geographic and Regulatory Challenges in CARLA
To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in high-dimensional environments characterized by intr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/12/6838 |
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