YOLO-MARL: Enhancing Multi-agent Reinforcement Learning with LLMs

[t1seungy]

Summary

YOLO-MARL is a novel framework that leverages LLMs for high-level task planning to improve the policy learning process in multi-agent reinforcement learning (MARL). It requires only a one-time interaction with LLMs, avoiding ongoing costs and computational time during training.

Implementation Guidance

• Explore the integration of YOLO-MARL framework in existing MARL environments.
• Evaluate the performance improvements in cooperative games using this approach.

Reference

YOLO-MARL: You Only LLM Once for Multi-agent Reinforcement Learning

Tags

• LLM
• Multi-agent Systems
• Reinforcement Learning

Assignee

@ComposioHQ