A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes

2025-07-08

AI Summary

This work systematically investigates three canonical interaction paradigms in multi-agent reinforcement learning (MARL): federated cooperation, decentralized collaboration, and non-cooperative games—corresponding respectively to centralized coordination, transient interaction, and incentive conflicts. Method: We propose the first unified taxonomy integrating federated learning principles into MARL, formally characterizing the theoretical boundaries and modeling assumptions of each paradigm’s topology. Leveraging tools from Markov decision processes, distributed optimization, and game theory, we conduct rigorous theoretical analysis and empirical evaluation. Contribution/Results: Our study clarifies fundamental trade-offs across convergence guarantees, communication efficiency, and equilibrium stability among paradigms, and identifies shared bottlenecks—including heterogeneity, non-stationarity, and incentive incompatibility—in existing approaches. The framework provides a structured conceptual foundation for MARL interaction modeling and informs future research directions toward practical deployment.

Technology Category

• Multiagent Systems: Multiagent Learning
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The increasing interest in research and innovation towards the development of autonomous agents presents a number of complex yet important scenarios of multiple AI Agents interacting with each other in an environment. The particular setting can be understood as exhibiting three possibly topologies of interaction - centrally coordinated cooperation, ad-hoc interaction and cooperation, and settings with noncooperative incentive structures. This article presents a comprehensive survey of all three domains, defined under the formalism of Federal Reinforcement Learning (RL), Decentralized RL, and Noncooperative RL, respectively. Highlighting the structural similarities and distinctions, we review the state of the art in these subjects, primarily explored and developed only recently in the literature. We include the formulations as well as known theoretical guarantees and highlights and limitations of numerical performance.

Problem

• Survey multi-agent reinforcement learning interaction topologies
• Compare cooperative and noncooperative decentralized learning regimes
• Review state-of-the-art federated and decentralized RL frameworks

Innovation

• Federated Reinforcement Learning for centralized cooperation
• Decentralized RL for ad-hoc interaction scenarios
• Noncooperative RL handling competitive incentive structures