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Optimizing Mixed Autonomy Traffic Flow with Decentralized Autonomous Vehicles and Multi-Agent Reinforcement Learning., , , and . ACM Trans. Cyber Phys. Syst., 7 (2): 13:1-13:22 (April 2023)Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data., , , , , and . CoRR, (2024)Fuel Consumption Reduction of Multi-Lane Road Networks using Decentralized Mixed-Autonomy Control., , , , and . ITSC, page 2068-2073. IEEE, (2021)Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data., , , , , and . ITSC, page 4346-4351. IEEE, (2023)Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world., , , , and . NeurIPS, (2022)Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs., , , , , , , , , and 54 other author(s). CoRR, (2024)Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers., , , , , , , , , and 18 other author(s). CoRR, (2021)Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test., , , , , , , , , and 3 other author(s). CoRR, (2024)Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data., , , , , and . ICRA, page 2884-2890. IEEE, (2022)Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL., , , and . CoRR, (2020)