Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning

2025-08-21

AI Summary

In multi-agent reinforcement learning (MARL), attributing individual contributions becomes challenging when explicit value feedback (e.g., rewards or value functions) is absent. To address this, this paper proposes an explainability framework grounded in causal analysis of policy distributions. Its core innovation is the information-theoretic expected cooperative value (ICV), which quantifies each agent’s causal influence on teammates’ instrumental empowerment via Shapley values—without requiring reward signals or value function estimates. The method jointly models policy distributions, assesses decision uncertainty, and measures preference alignment. Evaluated in cooperative and competitive game environments, it demonstrates robust causal reasoning capability. Experiments show that ICVs accurately identify strategy behaviors that promote team success, significantly enhancing both interpretability and attribution fidelity of coordination mechanisms in MARL systems.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

To reliably deploy Multi-Agent Reinforcement Learning (MARL) systems, it is crucial to understand individual agent behaviors within a team. While prior work typically evaluates overall team performance based on explicit reward signals or learned value functions, it is unclear how to infer agent contributions in the absence of any value feedback. In this work, we investigate whether meaningful insights into agent behaviors can be extracted that are consistent with the underlying value functions, solely by analyzing the policy distribution. Inspired by the phenomenon that intelligent agents tend to pursue convergent instrumental values, which generally increase the likelihood of task success, we introduce Intended Cooperation Values (ICVs), a method based on information-theoretic Shapley values for quantifying each agent's causal influence on their co-players' instrumental empowerment. Specifically, ICVs measure an agent's action effect on its teammates' policies by assessing their decision uncertainty and preference alignment. The analysis across cooperative and competitive MARL environments reveals the extent to which agents adopt similar or diverse strategies. By comparing action effects between policies and value functions, our method identifies which agent behaviors are beneficial to team success, either by fostering deterministic decisions or by preserving flexibility for future action choices. Our proposed method offers novel insights into cooperation dynamics and enhances explainability in MARL systems.

Problem

• Quantify agent causal influence on teammates' empowerment
• Analyze policy distribution without value function feedback
• Identify beneficial agent behaviors for team success

Innovation

• Information-theoretic Shapley values quantify influence
• Measures action effects via decision uncertainty assessment
• Compares policy-value alignment to identify beneficial behaviors