AI RESEARCH
Scouting By Reward: VLM-TO-IRL-Driven Player Selection For Esports
arXiv CS.LG
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ArXi:2604.14474v1 Announce Type: new Traditional esports scouting workflows rely heavily on manual video review and aggregate performance metrics, which often fail to capture the nuanced decision-making patterns necessary to determine if a prospect fits a specific tactical archetype. To address this, we reframe style-based player evaluation in esports as an Inverse Reinforcement Learning (IRL) problem. In this paper, we