hierarchical control

Hierarchical Control in Multi-Agent Games: LLM Planning with RL Execution Outperforms Flat Learning

Researchers propose a hierarchical architecture where a large language model (LLM) acts as a centralized strategic controller selecting among specialized RL skill policies for a team of agents. In a 2v2 King of the Hill environment, the LLM+RL system achieved a 46.4% win rate, statistically equivalent to hand-crafted behavior trees (51.5%), and significantly outperformed flat RL. A user study found 60% of participants perceived the LLM+RL agents as the most human-like.