Strategic Coordination for Evolving Multi-agent Systems: A Hierarchical Reinforcement and Collective Learning Approach

2025-09-22

AI Summary

This work addresses decentralized combinatorial optimization in dynamic multi-agent systems. We propose a hierarchical framework integrating reinforcement learning and collective learning: a high-level multi-agent reinforcement learning (MARL) module provides strategic guidance, while a low-level distributed collective learning mechanism enables cooperative decision-making. The architecture preserves agent autonomy while achieving scalability through action-space compression and minimal communication overhead, balancing long-term strategic planning, short-term collective performance, and environmental adaptability. Our key contribution is the first integration of MARL with decentralized collective learning to establish a scalable, Pareto-optimal evolutionary mechanism. Experiments in synthetic benchmarks and real-world smart-city applications—including energy self-management and drone swarm sensing—demonstrate significant improvements over standalone MARL or collective learning baselines, yielding superior optimization performance, system scalability, and robustness.

Technology Category

• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Decentralized combinatorial optimization in evolving multi-agent systems poses significant challenges, requiring agents to balance long-term decision-making, short-term optimized collective outcomes, while preserving autonomy of interactive agents under unanticipated changes. Reinforcement learning offers a way to model sequential decision-making through dynamic programming to anticipate future environmental changes. However, applying multi-agent reinforcement learning (MARL) to decentralized combinatorial optimization problems remains an open challenge due to the exponential growth of the joint state-action space, high communication overhead, and privacy concerns in centralized training. To address these limitations, this paper proposes Hierarchical Reinforcement and Collective Learning (HRCL), a novel approach that leverages both MARL and decentralized collective learning based on a hierarchical framework. Agents take high-level strategies using MARL to group possible plans for action space reduction and constrain the agent behavior for Pareto optimality. Meanwhile, the low-level collective learning layer ensures efficient and decentralized coordinated decisions among agents with minimal communication. Extensive experiments in a synthetic scenario and real-world smart city application models, including energy self-management and drone swarm sensing, demonstrate that HRCL significantly improves performance, scalability, and adaptability compared to the standalone MARL and collective learning approaches, achieving a win-win synthesis solution.

Problem

• Addresses decentralized combinatorial optimization in evolving multi-agent systems
• Solves exponential joint state-action space growth in multi-agent reinforcement learning
• Reduces communication overhead while preserving agent autonomy and privacy

Innovation

• Hierarchical framework combining MARL and collective learning
• High-level MARL strategies reduce action space for Pareto optimality
• Low-level collective learning enables decentralized coordination with minimal communication