AIBullisharXiv – CS AI · Jun 196/10
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Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Researchers propose a hierarchical multi-agent control architecture combining pretrained large language models for strategic planning with reinforcement learning policies for tactical execution. The hybrid LLM+RL system achieves competitive performance in complex multi-agent games while demonstrating superior human-like behavioral qualities compared to traditional RL and behavior tree approaches.