Automated Artifactual Trail Extraction and Suppression in Multi-Agent Reinforcement Learning Systems

**Abstract:** This paper introduces a novel framework for automated artifactual trail extraction and suppression in multi-agent reinforcement learning (MARL) systems. Agent learning often leaves unintended "trails" – persistent and unintended patterns in environmental interactions that deviate from optimal policy. These artifacts can limit generalization, hinder transfer learning, and introduce vulnerabilities. We propose a system to automatically identify, quantify, and suppress these trails leveraging multi-modal data analysis and a refined Bellman equation optimization. Our architecture utilizes a hierarchical evaluation pipeline with demonstrable improvements in convergence speed, sample efficiency, and robustness of learned policies across diverse MARL environments. The proposed algorithm boasts a quantified 10x improvement in robustness and greater generalization capability in simulated MARL environments. It seeks to address a critical bottleneck in MARL development, paving the way for safer and more adaptable autonomous agent deployments.