A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911094037020672 |
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| author | Han, Ye Zhang, Lijun Meng, Dejian Zhang, Zhuang Hu, Xingyu Weng, Songyu |
| author_facet | Han, Ye Zhang, Lijun Meng, Dejian Zhang, Zhuang Hu, Xingyu Weng, Songyu |
| contents | To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte Carlo tree search (MCTS) method with parallel update for multi-agent Markov game with limited horizon and time discounted setting. By analyzing the parallel actions in the multi-vehicle joint action space in the partial-steady-state traffic flow, the parallel update method can quickly exclude potential dangerous actions, thereby increasing the search depth without sacrificing the search breadth. The proposed method is tested in a large number of randomly generated traffic flow. The experiment results show that the algorithm has good robustness and better performance than the SOTA reinforcement learning algorithms and heuristic methods. The vehicle driving strategy using the proposed algorithm shows rationality beyond human drivers, and has advantages in traffic efficiency and safety in the coordinating zone. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_13783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles Han, Ye Zhang, Lijun Meng, Dejian Zhang, Zhuang Hu, Xingyu Weng, Songyu Multiagent Systems Artificial Intelligence Computer Science and Game Theory Systems and Control To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte Carlo tree search (MCTS) method with parallel update for multi-agent Markov game with limited horizon and time discounted setting. By analyzing the parallel actions in the multi-vehicle joint action space in the partial-steady-state traffic flow, the parallel update method can quickly exclude potential dangerous actions, thereby increasing the search depth without sacrificing the search breadth. The proposed method is tested in a large number of randomly generated traffic flow. The experiment results show that the algorithm has good robustness and better performance than the SOTA reinforcement learning algorithms and heuristic methods. The vehicle driving strategy using the proposed algorithm shows rationality beyond human drivers, and has advantages in traffic efficiency and safety in the coordinating zone. |
| title | A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles |
| topic | Multiagent Systems Artificial Intelligence Computer Science and Game Theory Systems and Control |
| url | https://arxiv.org/abs/2409.13783 |