Multi-Agent Reinforcement Learning with Focal Diversity Optimization

• URL: http://arxiv.org/abs/2502.04492v1
• Date: Thu, 06 Feb 2025 20:44:26 GMT
• Title: Multi-Agent Reinforcement Learning with Focal Diversity Optimization
• Authors: Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu, Zachary Yahn, Ling Liu,
• Abstract summary: We introduce a focal diversity-optimized multi-agent reinforcement learning approach, coined as MARL-Focal. Our model achieves performance improvement of 5.51% compared to the best individual LLM-agent.
• Score: 7.498844064516196
• License:
• Abstract: The advancement of Large Language Models (LLMs) and their finetuning strategies has triggered the renewed interests in multi-agent reinforcement learning. In this paper, we introduce a focal diversity-optimized multi-agent reinforcement learning approach, coined as MARL-Focal, with three unique characteristics. First, we develop an agent-fusion framework for encouraging multiple LLM based agents to collaborate in producing the final inference output for each LLM query. Second, we develop a focal-diversity optimized agent selection algorithm that can choose a small subset of the available agents based on how well they can complement one another to generate the query output. Finally, we design a conflict-resolution method to detect output inconsistency among multiple agents and produce our MARL-Focal output through reward-aware and policy-adaptive inference fusion. Extensive evaluations on five benchmarks show that MARL-Focal is cost-efficient and adversarial-robust. Our multi-agent fusion model achieves performance improvement of 5.51\% compared to the best individual LLM-agent and offers stronger robustness over the TruthfulQA benchmark. Code is available at https://github.com/sftekin/rl-focal

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