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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Qingshan Wang

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#reinforcement learning Jan 2027

Graph Transformer Networks for Driving Multiagent Decision-Making via Deep Reinforcement Learning

A graph transformer reinforcement learning (GTRL) framework, which aims to make reasonable lane-changing decisions on highways by modeling multiagent interactions, and demonstrates GTRL’s effectiveness in enhancing intelligent driving, leading to safer and more efficient driving outcomes.

Jia-Lin Liu, Xiao-Lan Wang, Yan-Song Wang et al. · 0 citations
Conference Jul 2026

STGraphVQA: spatial-temporal graph reasoning with hierarchical cognition for interpretable driving scene understanding

STGraphVQA, a framework that represents driving scenes as dynamic spatiotemporal graphs, where nodes denote traffic participants, edges encode spatial and semantic relationships, and the temporal dimension captures their evolution, provides a promising direction toward interpretable autonomous driving systems.

Dengquan Zhang, Jiayi Wu, Qingshan Wang et al. · 0 citations

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