PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917813606678528 |
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| author | Bokade, Rohit Jin, Xiaoning |
| author_facet | Bokade, Rohit Jin, Xiaoning |
| contents | Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face challenges such as slow simulation speeds and convoluted, difficult-to-maintain codebases. To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. PyTSC integrates multiple simulators, such as SUMO and CityFlow, and offers a streamlined API, empowering researchers to explore a broad spectrum of MARL approaches efficiently. PyTSC accelerates experimentation and provides new opportunities for advancing intelligent traffic management systems in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18202 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control Bokade, Rohit Jin, Xiaoning Multiagent Systems Artificial Intelligence Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face challenges such as slow simulation speeds and convoluted, difficult-to-maintain codebases. To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. PyTSC integrates multiple simulators, such as SUMO and CityFlow, and offers a streamlined API, empowering researchers to explore a broad spectrum of MARL approaches efficiently. PyTSC accelerates experimentation and provides new opportunities for advancing intelligent traffic management systems in real-world applications. |
| title | PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2410.18202 |