# Can VLMs Play Action Role-Playing Games? Take Black Myth Wukong as a Study Case

arXiv: https://arxiv.org/abs/2409.12889 · HTML: https://mlsanity.com/paper/2409.12889/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models](https://mlsanity.com/paper/2401.13919/) (2024)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Google Research Football: A Novel Reinforcement Learning Environment](https://mlsanity.com/paper/1907.11180/) (2019)
- [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning](https://mlsanity.com/paper/2405.10292/) (2024)
- [SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.07489/) (2022)
- [Symbolic Learning Enables Self-Evolving Agents](https://mlsanity.com/paper/2406.18532/) (2024)
- [CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents](https://mlsanity.com/paper/2401.10568/) (2024)

## Built on by (most cited first, 24 of 24)

- [Visual Large Language Models for Generalized and Specialized Applications](https://mlsanity.com/paper/2501.02765/) (2025)
- [G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning](https://mlsanity.com/paper/2505.13426/) (2025)
- [Are Large Vision Language Models Good Game Players?](https://mlsanity.com/paper/2503.02358/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks](https://mlsanity.com/paper/2604.20987/) (2026)
- [CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games](https://mlsanity.com/paper/2503.09527/) (2025)
- [V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models](https://mlsanity.com/paper/2504.06148/) (2025)
- [AutoUE: Automated Generation of 3D Games in Unreal Engine via Multi-Agent Systems](https://mlsanity.com/paper/2603.07106/) (2026)
- [LLaVA-UHD v2: an MLLM Integrating High-Resolution Semantic Pyramid via Hierarchical Window Transformer](https://mlsanity.com/paper/2412.13871/) (2024)
- [GameVerse: Can Vision-Language Models Learn from Video-based Reflection?](https://mlsanity.com/paper/2603.06656/) (2026)
- [MageBench: Bridging Large Multimodal Models to Agents](https://mlsanity.com/paper/2412.04531/) (2024)
- [EscapeCraft: A 3D Room Escape Environment for Benchmarking Complex Multimodal Reasoning Ability](https://mlsanity.com/paper/2503.10042/) (2025)
- [Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown](https://mlsanity.com/paper/2506.17589/) (2025)
- [FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games](https://mlsanity.com/paper/2509.01052/) (2025)
- [ProxyWar: Dynamic Assessment of LLM Code Generation in Game Arenas](https://mlsanity.com/paper/2602.04296/) (2026)
- [Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse](https://mlsanity.com/paper/2605.09965/) (2026)
- [AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents](https://mlsanity.com/paper/2606.24893/) (2026)
- [VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction](https://mlsanity.com/paper/2509.06736/) (2025)
- [HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Making](https://mlsanity.com/paper/2509.12927/) (2025)
- [Cultivating Game Sense for Yourself: Making VLMs Gaming Experts](https://mlsanity.com/paper/2503.21263/) (2025)
- [Data Metabolism: An Efficient Data Design Schema For Vision Language Model](https://mlsanity.com/paper/2504.12316/) (2025)
- [StarBench: A Turn-Based RPG Benchmark for Agentic Multimodal Decision-Making and Information Seeking](https://mlsanity.com/paper/2510.18483/) (2025)
- [GamerAstra: Supporting 2D Non-Twitch Video Games for Blind and Low-Vision Players through a Multi-Agent Framework](https://mlsanity.com/paper/2506.22937/) (2025)
- [Separating Decision Time from Decision Quality in the Real-Time Gap of Distilled Deciders: Evidence from a Game and a Conveyor Simulator](https://mlsanity.com/paper/2610.04810/) (2026)

## Abstract (arXiv)

Recently, large language model (LLM)-based agents have made significant advances across various fields. One of the most popular research areas involves applying these agents to video games. Traditionally, these methods have relied on game APIs to access in-game environmental and action data. However, this approach is limited by the availability of APIs and does not reflect how humans play games. With the advent of vision language models (VLMs), agents now have enhanced visual understanding capabilities, enabling them to interact with games using only visual inputs. Despite these advances, current approaches still face challenges in action-oriented tasks, particularly in action role-playing games (ARPGs), where reinforcement learning methods are prevalent but suffer from poor generalization and require extensive training. To address these limitations, we select an ARPG, ``Black Myth: Wukong'', as a research platform to explore the capability boundaries of existing VLMs in scenarios requiring visual-only input and complex action output. We define 12 tasks within the game, with 75% focusing on combat, and incorporate several state-of-the-art VLMs into this benchmark. Additionally, we will release a human operation dataset containing recorded gameplay videos and operation logs, including mouse and keyboard actions. Moreover, we propose a novel VARP (Vision Action Role-Playing) agent framework, consisting of an action planning system and a visual trajectory system. Our framework demonstrates the ability to perform basic tasks and succeed in 90% of easy and medium-level combat scenarios. This research aims to provide new insights and directions for applying multimodal agents in complex action game environments. The code and datasets will be made available at https://varp-agent.github.io/.