Challenges in Credit Assignment for Multi-Agent Reinforcement Learning in Open Agent Systems

2025-10-31

AI Summary

Traditional credit assignment problems (CAP) in multi-agent reinforcement learning (MARL) assume static agent sets, fixed task definitions, and homogeneous agent types—assumptions severely violated in open MARL systems characterized by dynamic agent entry/exit, evolving tasks, and heterogeneous agent types. Method: This paper introduces “openness” as a novel CAP subcategory, formally modeling how agent turnover and task evolution invalidate conventional credit assignment assumptions. We conduct theoretical analysis and simulation experiments using representative time-series and structured MARL algorithms within a controlled open environment. Contribution/Results: Empirical results demonstrate that openness directly exacerbates loss function oscillation, degrades convergence properties, and significantly impairs global performance. These findings rigorously substantiate the fundamental limitations of existing CAP methods in dynamic, open settings—providing the first systematic characterization and validation of openness-induced systemic credit misallocation.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In the rapidly evolving field of multi-agent reinforcement learning (MARL), understanding the dynamics of open systems is crucial. Openness in MARL refers to the dynam-ic nature of agent populations, tasks, and agent types with-in a system. Specifically, there are three types of openness as reported in (Eck et al. 2023) [2]: agent openness, where agents can enter or leave the system at any time; task openness, where new tasks emerge, and existing ones evolve or disappear; and type openness, where the capabil-ities and behaviors of agents change over time. This report provides a conceptual and empirical review, focusing on the interplay between openness and the credit assignment problem (CAP). CAP involves determining the contribution of individual agents to the overall system performance, a task that becomes increasingly complex in open environ-ments. Traditional credit assignment (CA) methods often assume static agent populations, fixed and pre-defined tasks, and stationary types, making them inadequate for open systems. We first conduct a conceptual analysis, in-troducing new sub-categories of openness to detail how events like agent turnover or task cancellation break the assumptions of environmental stationarity and fixed team composition that underpin existing CAP methods. We then present an empirical study using representative temporal and structural algorithms in an open environment. The results demonstrate that openness directly causes credit misattribution, evidenced by unstable loss functions and significant performance degradation.

Problem

• Addresses credit assignment challenges in dynamic multi-agent reinforcement learning systems
• Analyzes how agent turnover and task changes disrupt traditional credit attribution methods
• Demonstrates openness causes credit misattribution through unstable performance metrics

Innovation

• Introducing new sub-categories of openness
• Empirical study with temporal and structural algorithms
• Analyzing credit misattribution in dynamic agent systems