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UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors
This paper introduces a reinforcement learning framework that enables controllable and diverse player behaviors without relying on human gameplay data. Existing approaches often require large-scale player trajectories, train separate models for different player types, or provide no direct mapping be
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