arXiv Machine Learning September 11, 2026 By Atahan Cilan, Atay \"Ozg\"ovde

UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors

Read the original on arXiv Machine Learning →

The paper presents UBCL, a reinforcement learning framework that generates controllable and diverse player behaviors without using human gameplay data. By defining behavior in an N‑dimensional continuous space and training a single PPO‑based multi‑agent policy with target behavior vectors, the method learns how actions affect behavioral statistics such as aggressiveness, mobility, and cooperativeness. Experiments in a custom Unity multiplayer game demonstrate that UBCL achieves greater behavioral diversity than a win‑only baseline and accurately matches specified behavior vectors across a range of targets.

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