arXiv AI June 19, 2026 By Jannik H\"osch, Alessandro Sestini, Florian Fuchs, Amir Baghi, Joakim Bergdahl, Konrad Tollmar, Jean-Philippe Barrette-LaPierre, Linus Gissl\'en

Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution

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arXiv:2606. 20014v1 Announce Type: cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Related stories

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents

IB-RL: Isolated Bilateral Reinforcement Learning for Strategic Dialogue Agents

Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models