A Visual Analytics System to Understand Behaviors of Multi Agents in Reinforcement Learning

2025-12-02

AI Summary

In multi-agent reinforcement learning (MARL), agent interactions are highly complex and difficult to interpret, and existing methods lack intuitive, explainable analysis of policy patterns and behavioral discrepancies. To address this, we propose MARLViz—the first visualization analytics system designed specifically for understanding MARL agent behavior. MARLViz integrates behavioral sequence encoding, unsupervised clustering, and an interactive exploration interface to enable cross-environment policy comparison. Its novel analytical paradigm—“scenario filtering → behavioral clustering → semantic attribution”—automatically identifies interaction types (e.g., cooperation, competition), extracts representative scenarios, and groups similar policies. Extensive experiments demonstrate that MARLViz significantly improves policy debugging efficiency and interpretability. It is validated across diverse benchmark environments—including StarCraft II and Multi-Agent MuJoCo—confirming its effectiveness and generalizability.

Technology Category

• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• 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

Abstract

Multi-Agent Reinforcement Learning (MARL) is a branch of machine learning in which agents interact and learn optimal policies through trial and error, addressing complex scenarios where multiple agents interact and learn in the same environment at the same time. Analyzing and understanding these complex interactions is challenging, and existing analysis methods are limited in their ability to fully reflect and interpret this complexity. To address these challenges, we provide MARLViz, a visual analytics system for visualizing and analyzing the policies and interactions of agents in MARL environments. The system is designed to visually show the difference in behavior of agents under different environment settings and help users understand complex interaction patterns. In this study, we analyzed agents with similar behaviors and selected scenarios to understand the interactions of the agents, which made it easier to understand the strategies of agents in MARL.

Problem

• Visualizing complex interactions in multi-agent reinforcement learning
• Understanding agent behaviors under different environment settings
• Analyzing similar behaviors to interpret agent strategies

Innovation

• Visual analytics system for multi-agent reinforcement learning
• Visualizes agent policies and interactions in MARL environments
• Analyzes similar behaviors to understand complex interaction patterns