SUMO Traffic Simulation Environment
收藏arXiv2025-09-30 收录
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https://sumo.dlr.de/docs/index.html
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资源简介：
该数据集是在使用城市移动模拟（SUMO）创建的模拟环境中构建的，旨在测试和评估深度强化学习代理在交通场景中的性能。该环境能够模拟多种参数，如状态空间、动作空间，并且与OpenAI GYM的无缝集成使得可以评估强化学习代理。该数据集的规模包含300个单元和3条车道，任务是通过深度强化学习进行交通优化和变道决策。
This dataset is constructed in a simulated environment created using the urban mobility simulator (SUMO), aiming to test and evaluate the performance of deep reinforcement learning agents in traffic scenarios. The environment supports simulation of multiple parameters such as state space and action space, and its seamless integration with OpenAI Gym enables the evaluation of reinforcement learning agents. This dataset includes 300 units and 3 lanes, with the task of conducting traffic optimization and lane-changing decisions via deep reinforcement learning.
提供机构：
Simulation of Urban Mobility (SUMO)
