MAESTRO: Multi-Agent Environment Shaping through Task and Reward Optimization

• URL: http://arxiv.org/abs/2511.19253v1
• Date: Mon, 24 Nov 2025 16:05:37 GMT
• Title: MAESTRO: Multi-Agent Environment Shaping through Task and Reward Optimization
• Authors: Boyuan Wu,
• Abstract summary: Existing approaches rely on fixed-generated Large Language Models (LLMs) directly in the control loop.<n>We propose MAESTRO, a framework that moves the LLM outside the execution loop and uses it as an offline training architect.<n>We evaluate MAESTRO on large-scale traffic signal control (Hangzhou, 16 intersections) and conduct controlled ablations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) faces two major design bottlenecks: crafting dense reward functions and constructing curricula that avoid local optima in high-dimensional, non-stationary environments. Existing approaches rely on fixed heuristics or use Large Language Models (LLMs) directly in the control loop, which is costly and unsuitable for real-time systems. We propose MAESTRO (Multi-Agent Environment Shaping through Task and Reward Optimization), a framework that moves the LLM outside the execution loop and uses it as an offline training architect. MAESTRO introduces two generative components: (i) a semantic curriculum generator that creates diverse, performance-driven traffic scenarios, and (ii) an automated reward synthesizer that produces executable Python reward functions adapted to evolving curriculum difficulty. These components guide a standard MARL backbone (MADDPG) without increasing inference cost at deployment. We evaluate MAESTRO on large-scale traffic signal control (Hangzhou, 16 intersections) and conduct controlled ablations. Results show that combining LLM-generated curricula with LLM-generated reward shaping yields improved performance and stability. Across four seeds, the full system achieves +4.0% higher mean return (163.26 vs. 156.93) and 2.2% better risk-adjusted performance (Sharpe 1.53 vs. 0.70) over a strong curriculum baseline. These findings highlight LLMs as effective high-level designers for cooperative MARL training.

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