cMALC-D uses an LLM to generate training contexts for multi-agent RL and a diversity-blending mechanism to avoid mode collapse, claiming improved generalization on traffic signal control.
Prompt to transfer: Sim-to-real transfer for traffic signal control with prompt learning
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cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending
cMALC-D uses an LLM to generate training contexts for multi-agent RL and a diversity-blending mechanism to avoid mode collapse, claiming improved generalization on traffic signal control.