SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem
This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the...
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sg-smu-ink.sis_research-97072024-04-04T09:11:07Z SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem GUO, Honglian HE, Zhi SHENG, Wenda CAO, Zhiguang ZHOU, Yingjie GAO, Weinan This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac to two real metropolitan transportation networks, namely Chengdu and Beijing, using real traffic data, with satisfying results. 2024-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8704 info:doi/10.1109/TITS.2024.3361445 https://ink.library.smu.edu.sg/context/sis_research/article/9707/viewcontent/T_ITS_SEGAC_final.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Gaussian distribution Generalized actor critic Navigation Optimization Real-time systems Reliability Routing sample efficiency stochastic on-time arrival (SOTA) Transportation variance reduction Operations Research, Systems Engineering and Industrial Engineering Theory and Algorithms Transportation |
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Gaussian distribution Generalized actor critic Navigation Optimization Real-time systems Reliability Routing sample efficiency stochastic on-time arrival (SOTA) Transportation variance reduction Operations Research, Systems Engineering and Industrial Engineering Theory and Algorithms Transportation |
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Gaussian distribution Generalized actor critic Navigation Optimization Real-time systems Reliability Routing sample efficiency stochastic on-time arrival (SOTA) Transportation variance reduction Operations Research, Systems Engineering and Industrial Engineering Theory and Algorithms Transportation GUO, Honglian HE, Zhi SHENG, Wenda CAO, Zhiguang ZHOU, Yingjie GAO, Weinan SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem |
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This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac to two real metropolitan transportation networks, namely Chengdu and Beijing, using real traffic data, with satisfying results. |
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text |
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GUO, Honglian HE, Zhi SHENG, Wenda CAO, Zhiguang ZHOU, Yingjie GAO, Weinan |
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GUO, Honglian HE, Zhi SHENG, Wenda CAO, Zhiguang ZHOU, Yingjie GAO, Weinan |
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GUO, Honglian |
title |
SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem |
title_short |
SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem |
title_full |
SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem |
title_fullStr |
SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem |
title_full_unstemmed |
SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem |
title_sort |
segac: sample efficient generalized actor critic for the stochastic on-time arrival problem |
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Institutional Knowledge at Singapore Management University |
publishDate |
2024 |
url |
https://ink.library.smu.edu.sg/sis_research/8704 https://ink.library.smu.edu.sg/context/sis_research/article/9707/viewcontent/T_ITS_SEGAC_final.pdf |
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