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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.

arXivUBCLNPC behaviorgame AIhuman gameplay datareinforcement-learning

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGUBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors ↗SEP 11 04:00 UTC

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