[2603.24324] Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning

Computer Science > Machine Learning arXiv:2603.24324 (cs) [Submitted on 25 Mar 2026] Title:Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning Authors:Dogan Urgun, Gokhan Gungor View a PDF of the paper titled Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning, by Dogan Urgun and Gokhan Gungor View PDF HTML (experimental) Abstract:Designing effective auxiliary rewards for cooperative multi-agent systems remains a precarious task; misaligned incentives risk inducing suboptimal coordination, especially where sparse task feedback fails to provide sufficient grounding. This study introduces an automated reward design framework that leverages large language models to synthesize executable reward programs from environment instrumentation. The procedure constrains candidate programs within a formal validity envelope and evaluates their efficacy by training policies from scratch under a fixed computational budget; selection depends exclusively on the sparse task return. The framework is evaluated across four distinct Overcooked-AI layouts characterized by varied corridor congestion, handoff dependencies, and structural asymmetries. Iterative search generations consistently yield superior task returns and delivery counts, with the most pronounced gains occurring in environments dominated by interaction bottlenecks. Diagnostic analysis of the synthesized shaping componen...