Multi-Agent Diagnostics for Robustness via Illuminated Diversity
(2401.13460)Abstract
In the rapidly advancing field of multi-agent systems, ensuring robustness in unfamiliar and adversarial settings is crucial. Notwithstanding their outstanding performance in familiar environments, these systems often falter in new situations due to overfitting during the training phase. This is especially pronounced in settings where both cooperative and competitive behaviours are present, encapsulating a dual nature of overfitting and generalisation challenges. To address this issue, we present Multi-Agent Diagnostics for Robustness via Illuminated Diversity (MADRID), a novel approach for generating diverse adversarial scenarios that expose strategic vulnerabilities in pre-trained multi-agent policies. Leveraging the concepts from open-ended learning, MADRID navigates the vast space of adversarial settings, employing a target policy's regret to gauge the vulnerabilities of these settings. We evaluate the effectiveness of MADRID on the 11vs11 version of Google Research Football, one of the most complex environments for multi-agent reinforcement learning. Specifically, we employ MADRID for generating a diverse array of adversarial settings for TiZero, the state-of-the-art approach which "masters" the game through 45 days of training on a large-scale distributed infrastructure. We expose key shortcomings in TiZero's tactical decision-making, underlining the crucial importance of rigorous evaluation in multi-agent systems.
Overview
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Multi-agent systems are crucial for AI applications but can be prone to errors in challenging situations due to overfitting.
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The paper presents MADRID, a system designed to assess and improve the robustness of multi-agent systems by exposing them to diverse adversarial scenarios.
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MADRID uses quality-diversity and MAP-Elites to explore adversarial settings, enhancing multi-agent performance by identifying and addressing strategic weaknesses.
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Experiments with the Google Research Football environment showed that MADRID can reveal significant flaws in the TiZero multi-agent policy.
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MADRID emphasizes the need for rigorous assessment of multi-agent systems to identify latent vulnerabilities and supports the development of more resilient AI.
Introduction
Multi-agent systems are pivotal for a variety of AI applications, notably those involving interactions with humans. However, their robustness is often challenged in unfamiliar or adversarial situations due to overfitting during training. This paper introduces Multi-Agent Diagnostics for Robustness via Illuminated Diversity (MADRID), aiming to generate diverse adversarial scenarios to diagnose strategic errors in pre-trained multi-agent policies. Integrating concepts from open-ended learning, MADRID identifies vulnerabilities using a target policy's regret, demonstrating its efficacy within the complex environment of Google Research Football.
Methodology
MADRID's foundation lies in the framework of quality-diversity (QD), which involves creating numerous high-quality solutions each with unique attributes. The paper details the use of MAP-Elites, a QD method that explores vast spaces of adversarial settings. This exploration is structured through discretization, mutation, and evaluation processes enhancing the diversity and performance of adversarial scenarios. With the use of a target policy's regret as a measurement tool, MADRID uncovers situations where reference policies outperform the target, thus illuminating potential strategic flaws.
Experimental Validation
The paper's empirical exploration concentrates on the 11 vs 11 setup from Google Research Football, analyzing MADRID against TiZero, a leading multi-agent RL approach. Results demonstrate that MADRID successfully uncovers critical weaknesses in TiZero's tactical decision-making. The scenarios created by MADRID reveal TiZero's inability to cope with specific adversarial settings, such as improper handling of the offside rule and a tendency towards own goals. The study underscores the necessity for rigorous evaluation in multi-agent systems to enhance overall robustness.
Analysis and Insights
The paper offers a qualitative analysis of adversarial levels identified by MADRID, delving into the nuanced shortcomings of the TiZero policy. It becomes evident that high-regret levels are often associated with poor strategic choices such as incorrect ball passing or positions for shooting. The findings affirm that multi-agent systems, even after extensive training, harbor latent vulnerabilities. MADRID's methodology not only showcases these vulnerabilities but also provides a means for future refinement of multi-agent strategies. This research stands as a testament to the importance of diagnosing and addressing such strategic errors for the progression of resilient multi-agent systems in AI.
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