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Open access Mar 2025

Multi-Agent Uncertainty-Aware Pessimistic Model-Based Reinforcement Learning for Connected Autonomous Vehicles

MA-PMBRL, a novel Multi-Agent Pessimistic Model-Based Reinforcement Learning framework for CAVs, incorporating a max-min optimization approach to enhance robustness and decision-making is proposed, demonstrating that the proposed framework represents a significant step toward scalable, efficient, and reliable multi-age...

Ruo-Qi Wen, Rong-Peng Li, Xing Xu et al. · 1 citation
Preprint Aug 2026

Agentic Router: An Execution-Grounded Continual Learning Approach With Memory

An execution-grounded dual-path consequence-aware agent for CLI-based SONiC operations, which generates multiple complete actions, predicts their execution consequences, and selects the final action through utility- and risk-aware reranking is proposed.

Yuxuan Chen, Rong-Peng Li, Zhi-Feng Zhao et al. · 0 citations

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