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human gameplay data

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papersSEP 11 04:00 UTC

UBCL reinforcement learning framework generates diverse game player behaviors without human data

A new arXiv paper presents UBCL, a reinforcement learning framework for producing controllable and varied non-player character behaviors in games. The authors note that existing methods typically depend on large collections of recorded human play or require training separate models for different behavior types. UBCL aims to remove the need for such gameplay data while keeping behaviors steerable and diverse.

reinforcement-learning/game-ai/agents/research