Multi-agent deep reinforcement learning for large-scale traffic signal control

T Chu, J Wang, L Codecà, Z Li - IEEE transactions on intelligent …, 2019 - ieeexplore.ieee.org
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …
被引用次数：798 相关文章

Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control

T Chu, J Wang, L Codecà, Z Li - arXiv e-prints, 2019 - ui.adsabs.harvard.edu
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …

[引用][C] Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control

T Chu, J Wang, L Codeca, Z Li - IEEE Transactions on Intelligent …, 2020 - cir.nii.ac.jp
Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control | CiNii
Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索フォームへ移動 …

Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control

T Chu, J Wang, L Codecà, Z Li - IEEE Transactions on Intelligent …, 2020 - trid.trb.org
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …

Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control

T Chu, J Wang, L Codecà, Z Li - arXiv preprint arXiv:1903.04527, 2019 - arxiv.org
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …