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Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion.

, , , , and . ITSC, page 759-765. IEEE, (2018)

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Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design., , , , , , and . NeurIPS, (2020)Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world., , , , and . NeurIPS, (2022)Emergent Behaviors in Mixed-Autonomy Traffic., , , and . CoRL, volume 78 of Proceedings of Machine Learning Research, page 398-407. PMLR, (2017)A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings., , , , , and . CoRR, (2021)Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data., , , , , and . ICRA, page 2884-2890. IEEE, (2022)Multi-lane reduction: A stochastic single-lane model for lane changing., , , and . ITSC, page 1-8. IEEE, (2017)Framework for control and deep reinforcement learning in traffic., , , , , , and . ITSC, page 1-8. IEEE, (2017)Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion., , , , and . ITSC, page 759-765. IEEE, (2018)GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS., , , , and . CoRR, (2024)Stabilizing Unsupervised Environment Design with a Learned Adversary., , , , , and . CoLLAs, volume 232 of Proceedings of Machine Learning Research, page 270-291. PMLR, (2023)