Valerant: An Automatic Navigable Game Map Generator via Action-Conditioned World Model Exploration
World Action Models (WAMs) couple predictive world modeling with action generation, allowing anticipated future states to guide agent behavior. Although WAMs are rapidly advancing embodied AI, general-purpose counterparts remain largely unexplored in games. Existing game-oriented approaches often combine action-conditioned world models with external policies and reward functions to realize WAM-like decision-making, yet they operate mainly in 2D visual observation space and do not instantiate persistent 3D geometry. Extending this paradigm to 3D games introduces a distinct challenge. In autonomous driving and robotics, the physical environment exists independently of the model, providing a...
| Authors | Yiran Qiao, Feng Wang, Jing Ma |
|---|---|
| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 23 September 2026 |
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