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Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning

reinforcement-learning › multi-agent

📄 Abstract

Abstract: Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially in the number of agents. These difficulties are exacerbated when balancing sequential global decision-making with local agent interactions. In this work, we propose a new algorithm $\texttt{SUBSAMPLE-MFQ}$ ($\textbf{Subsample}$-$\textbf{M}$ean-$\textbf{F}$ield-$\textbf{Q}$-learning) and a decentralized randomized policy for a system with $n$ agents. For any $k\leq n$, our algorithm learns a policy for the system in time polynomial in $k$. We prove that this learned policy converges to the optimal policy on the order of $\tilde{O}(1/\sqrt{k})$ as the number of subsampled agents $k$ increases. In particular, this bound is independent of the number of agents $n$.

Authors (3)

Emile Anand

Ishani Karmarkar

Guannan Qu

Submitted

December 1, 2024

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Introduces SUBSAMPLE-MFQ, a novel MARL algorithm that learns policies for systems with 'n' agents in time polynomial in 'k' (where k <= n), effectively decoupling learning complexity from the total number of agents. It provides theoretical convergence guarantees to the optimal policy as 'k' increases, independent of 'n'.

Business Value

Enables more efficient and scalable coordination of large fleets of autonomous agents (e.g., drones, robots) for tasks like logistics, exploration, or swarm control, reducing computational overhead.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Feasible for systems where agents can communicate or estimate mean-field information. Decentralized policy aids deployment.

Limitations Addressed

The fundamental challenge in MARL posed by the exponential growth of joint state and action spaces, and the difficulty in balancing global decision-making with local agent interactions.

Performance Gains

Achieves convergence to the optimal policy on the order of $\tilde{O}(1/\sqrt{k})$, where the bound is independent of the total number of agents 'n'.

Technical Tags

multi-agent reinforcement learningMARLmean-field Q-learningdecentralized policysubsamplingexponential state-action spacesequential decision-makinglocal interactionsconvergence bounds

Research Topics

Multi-Agent SystemsReinforcement LearningGame TheoryDistributed Decision Making

Methods & Architectures

SUBSAMPLE-MFQ (Subsample Mean-Field Q-learning)Decentralized randomized policyMean-field approximation Q-learning based agent policies

Applications & Tasks

Robotics Autonomous Systems Game AI Networked Systems Scalability of MARL algorithmsBalancing global decisions with local interactionsExponential growth of joint state-action spaces Cooperative multi-agent controlLearning optimal policies in large-scale MARLDecentralized coordination

Related Fields

Distributed SystemsControl TheoryEconomics (Game Theory)

Keywords

multi-agent reinforcement learningMARLmean-fieldQ-learningdecentralized controlscalabilitycooperative agentssubsamplingconvergenceexponential state space

Academic Context

#Multi-Agent Systems#Reinforcement Learning#Game Theory#Distributed Decision Making

Commercial Potential

Potential Products

Scalable coordination systems for autonomous vehicle fleetsAI for large-scale simulations (e.g., traffic, crowd dynamics)Advanced game AI for complex multi-player scenarios

Target Industries

RoboticsAutonomous VehiclesLogisticsGamingSmart Grids

Use Case Examples

Coordinating a swarm of drones for search and rescueOptimizing traffic flow in a city using connected vehiclesManaging energy distribution in a smart grid with multiple producers/consumers

Competitive Edge

Offers a significant theoretical and practical advantage in scalability for MARL problems compared to methods that scale with the number of agents.

Market Opportunity

Significant growth in multi-agent systems research and applications.

Revenue Models

Licensing of algorithmsdevelopment of specialized multi-agent control software.

Resource Requirements

Compute Needs

Potentially high during training, but inference can be decentralized and efficient.

Data Requirements

Requires simulation environments or real-world interaction data for training.

Deployment Constraints

Requires agents to be able to estimate or communicate mean-field information; assumes cooperative setting.

Scalability

Key contribution is improved scalability with respect to the number of agents 'n'.

Regulatory Considerations

Ethical considerations for autonomous agent coordination in safety-critical applications.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years for complex real-world deployments.

Patent Potential

Low to moderate, depending on specific implementation details.

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