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ROMA-iQSS: An Objective Alignment Approach via State-Based Value Learning and ROund-Robin Multi-Agent Scheduling

Chi-Hui Lin, Joewie J. Koh, Alessandro Roncone, Lijun Chen

TL;DR

This work tackles scalable decentralized coordination in multi-agent reinforcement learning by addressing two core challenges: identifying objective functions that maximize collective utility and aligning agents' goals without centralized policy sharing. It introduces ROMA-iQSS, a framework that combines independent state-based value learning (iQSS) with a round-robin multi-agent scheduling (ROMA) protocol to enable agents to autonomously discover optimal states and implicitly align their objectives. The authors prove convergence properties for iQSS under their set-equivalence assumption and show that ROMA fosters objective alignment, yielding a joint optimal policy. Empirical results across multi-agent coordination tasks demonstrate that ROMA-iQSS outperforms state-of-the-art centralized and decentralized baselines (e.g., I2Q, indQ) in identifying optimal states and achieving high, stable rewards. This approach offers a scalable path toward robust decentralized coordination in dynamic environments such as warehouses and autonomous driving, with potential extensions to human-robot interactions.

Abstract

Effective multi-agent collaboration is imperative for solving complex, distributed problems. In this context, two key challenges must be addressed: first, autonomously identifying optimal objectives for collective outcomes; second, aligning these objectives among agents. Traditional frameworks, often reliant on centralized learning, struggle with scalability and efficiency in large multi-agent systems. To overcome these issues, we introduce a decentralized state-based value learning algorithm that enables agents to independently discover optimal states. Furthermore, we introduce a novel mechanism for multi-agent interaction, wherein less proficient agents follow and adopt policies from more experienced ones, thereby indirectly guiding their learning process. Our theoretical analysis shows that our approach leads decentralized agents to an optimal collective policy. Empirical experiments further demonstrate that our method outperforms existing decentralized state-based and action-based value learning strategies by effectively identifying and aligning optimal objectives.

ROMA-iQSS: An Objective Alignment Approach via State-Based Value Learning and ROund-Robin Multi-Agent Scheduling

TL;DR

This work tackles scalable decentralized coordination in multi-agent reinforcement learning by addressing two core challenges: identifying objective functions that maximize collective utility and aligning agents' goals without centralized policy sharing. It introduces ROMA-iQSS, a framework that combines independent state-based value learning (iQSS) with a round-robin multi-agent scheduling (ROMA) protocol to enable agents to autonomously discover optimal states and implicitly align their objectives. The authors prove convergence properties for iQSS under their set-equivalence assumption and show that ROMA fosters objective alignment, yielding a joint optimal policy. Empirical results across multi-agent coordination tasks demonstrate that ROMA-iQSS outperforms state-of-the-art centralized and decentralized baselines (e.g., I2Q, indQ) in identifying optimal states and achieving high, stable rewards. This approach offers a scalable path toward robust decentralized coordination in dynamic environments such as warehouses and autonomous driving, with potential extensions to human-robot interactions.

Abstract

Effective multi-agent collaboration is imperative for solving complex, distributed problems. In this context, two key challenges must be addressed: first, autonomously identifying optimal objectives for collective outcomes; second, aligning these objectives among agents. Traditional frameworks, often reliant on centralized learning, struggle with scalability and efficiency in large multi-agent systems. To overcome these issues, we introduce a decentralized state-based value learning algorithm that enables agents to independently discover optimal states. Furthermore, we introduce a novel mechanism for multi-agent interaction, wherein less proficient agents follow and adopt policies from more experienced ones, thereby indirectly guiding their learning process. Our theoretical analysis shows that our approach leads decentralized agents to an optimal collective policy. Empirical experiments further demonstrate that our method outperforms existing decentralized state-based and action-based value learning strategies by effectively identifying and aligning optimal objectives.
Paper Structure (15 sections, 4 theorems, 42 equations, 3 figures, 4 algorithms)

This paper contains 15 sections, 4 theorems, 42 equations, 3 figures, 4 algorithms.

Table of Contents

  1. INTRODUCTION
  2. Preliminaries
  3. Coordination Problem
  4. Overview of Value Learning Methods
  5. Centralized Q Learning (cenQ)
  6. Independent Q Learning (indQ)
  7. Centralized QSS Learning (cQSS)
  8. Methods
  9. Independent QSS Value Learning (iQSS)
  10. Multiagent Interaction Schemes
  11. Synchronous Multiagent Interaction (SMA)
  12. Round-Robin Multiagent Interaction (ROMA)
  13. A Practical iQSS Learning Process
  14. Numerical Experiments
  15. Conclusion and Discussion

Key Result

Theorem III.1

$\pi^{ss*}_k$ is optimal under the set equivalence assumption:

Figures (3)

  • Figure 1: Motivating example: five agents are tasked with a collaborative transport problem where a large object needs to be moved to designated locations marked by flags. In the case of failed coordination, the left subgroup (two agents), struggles to identify the optimal objectives, while the right subgroup (three agents), identifies the objectives but fails to align their efforts effectively, leading to an undesired final location for the object. In contrast, successful coordination manifests when all agents not only identify the optimal objectives but also achieve precise alignment in their efforts, culminating in the object reaching its intended location.
  • Figure 2: Problem Definition: This is a 2-agent game featuring four potential destination states, each represented by two numbers denoting Agent X's and Agent Y's policies. In this game, both agents can identify optimal states, indicated in red text, but they make their decisions independently without knowledge of each other's choices. SMA-Scenario(Left): Both agents observe all potential states, including two optimal states. Consequently, they might establish divergent objectives, which prevents them from reaching either of the two optimal states. ROMA-Scenario(Right): Agent X observes all potential states and selects its policy accordingly. Agent Y, on the other hand, observes only the states that Agent X's selection can lead to. Consequently, Agent X's selection influences Agent Y's choice, enabling it to align with Agent X's objective and ultimately reach the optimal state.
  • Figure 3: Teams of 3, 5, and 7 agents navigate three-stage coordination, targeting top outcomes in an environment with multiple optimal strategies.

Theorems & Definitions (8)

  • Theorem III.1
  • proof
  • Lemma III.2
  • proof
  • Lemma III.3
  • proof
  • Theorem III.4
  • proof