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This article reviews the evolution of game AI agents, examines how large language models (LLMs) can drive new AI behaviors in games, and discusses practical case studies across genres such as Werewolf‑style, war‑SLG, and MOBA games, concluding with challenges and future research directions.
The paper presents DouZero, a reinforcement‑learning AI for the Chinese card game Dou Dizhu that combines a Monte‑Carlo method with a value network, uses binary matrix encodings for states and actions, and achieves human‑level play and state‑of‑the‑art results on modest GPU hardware.
This article reviews the challenges of model‑free reinforcement learning, especially sparse reward issues exemplified by Montezuma’s Revenge, and surveys recent approaches such as expert demonstrations, curriculum learning, self‑play, hierarchical reinforcement learning, and count‑based exploration to mitigate these problems.