Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration

Abstract: We propose a new framework for multi-agent reinforcement learning (MARL), where the agents cooperate in a time-evolving network with latent community structures and mixed memberships. Unlike traditional neighbor-based or fixed interaction graphs, our community-based framework captures flexible and abstract coordination patterns by allowing each agent to belong to multiple overlapping communities. Each community maintains shared policy and value functions, which are aggregated by individual agents according to personalized membership weights. We also design actor-critic algorithms that exploit this structure: agents inherit community-level estimates for policy updates and value learning, enabling structured information sharing without requiring access to other agents' policies. Importantly, our approach supports both transfer learning by adapting to new agents or tasks via membership estimation, and active learning by prioritizing uncertain communities during exploration. Theoretically, we establish convergence guarantees under linear function approximation for both actor and critic updates. To our knowledge, this is the first MARL framework that integrates community structure, transferability, and active learning with provable guarantees.

Community-Based Multi-Agent Reinforcement Learning with Transfer and Active Exploration

The paper "Community-Based Multi-Agent Reinforcement Learning with Transfer and Active Exploration" presents an innovative framework designed to enhance Multi-Agent Reinforcement Learning (MARL). Unlike traditional approaches that focus on fixed or neighbor-based interaction graphs, this framework introduces a dynamic network wherein agents participate in multiple communities, each contributing distinct policies and value functions. This setup aims to better capture the abstract and complex cooperation patterns found in real-world systems.

Overview

Research Context

Recent advancements in MARL have highlighted numerous challenges, primarily those related to agent heterogeneity and dynamic, complex environments involving many individuals. Traditional MARL approaches often adopt the simplified assumption of independent learners or centralized training methods that overlook nuanced interaction structures. They fail to encapsulate patterns such as overlapping roles or localized cooperation crucial for scalability and generalization across various settings.

In response, networked interaction structures have been integrated into MARL, enabling decentralized actor-critic frameworks and improving scalable consensus methods. Previous research has utilized graph neural networks and structured exploration but often relies on fixed interaction graphs, which restrict coordination to predefined local neighbors. This paper proposes a community-based perspective, offering a more expressive framework that captures latent interaction structures and facilitates both policy transfer learning and active learning.

Community-Based Framework

In the proposed framework, each agent has mixed memberships across multiple communities, where each community is associated with distinct policy and value functions. Agents aggregate community policies based on personalized membership weights, aiming to maximize average rewards across the network. This method supports structured information sharing and inherently facilitates transfer learning by adapting to new agents or tasks through membership estimation.

Algorithm Design

The primary contribution of this paper is the design of actor-critic algorithms tailored to the community-based MARL structure. The critic updates community-level value functions via temporal difference learning, and agents inherit this structured knowledge. The actor updates individual policies using personalized advantage estimates derived from mixed memberships. This design supports community-guided exploration, prioritizing uncertain communities to enhance sample efficiency, effectively integrating active learning principles.

Strong Numerical Results

The proposed algorithms offer convergence guarantees under linear function approximation for both actor and critic updates, a significant theoretical contribution not observed in prior MARL frameworks. Through numerical simulations, the paper demonstrates improved globally averaged returns compared to neighbor-based models, especially in scenarios where agent rewards are aligned with community roles rather than local interactions.

Implications

This research opens practical pathways in complex real-world applications, such as smart grid energy management, ride-sharing dispatch systems, and health care delivery systems. It also has theoretical implications for learning dynamics in large-scale, structured MARL environments.

Future Directions

The paper acknowledges limitations and proposes future research avenues, particularly concerning community estimation in dynamic settings and the application of deep function approximators. These advancements may enhance adaptability in highly dynamic or nonlinear environments, a crucial step towards deploying MARL solutions in diverse real-world contexts.

In summary, the community-based MARL framework represents an advancement in structuring agent interactions, deliberately addressing scalability, transferability, and efficient exploration in reinforcement learning. This leap promises to better reflect the latent coordination structures native to human systems, paving the way for more sophisticated, capable AI solutions.