arXiv cs.LG
· Papers
Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
arXiv:2606.20014v2 Announce Type: replace Abstract: Reinforcement learning (RL) has achieved strong performance in sequential decision-making, yet scaling to complex multi-agent environments remains challenging due to sparse rewards, large state-action spaces, and the difficulty of learning coordinated strategies. We p