Agora-1: The Multi-Agent World Model
Agora-1 is a multi-agent world model that learns to evolve game states and render them visually. It operates on the internal state of games, specifically GoldenEye, without relying on hard-coded logic. This system allows for the manipulation of game states to create new levels while maintaining gameplay dynamics.
- ▪Agora-1 learns how the world state evolves over time in response to player interaction.
- ▪It uses a model trained on the internal state of games, specifically GoldenEye.
- ▪The system can generate new levels while preserving gameplay dynamics consistent with the source games.
Opening excerpt (first ~120 words) tap to expand
Agora-1 learns two distinct functions. First, it learns how the world state evolves over time in response to player interaction. To do this, we train a model directly on the internal state of one or more games—in the case of Agora-1, GoldenEye. This model learns the underlying gameplay dynamics and how state transitions occur from player actions. Second, Agora-1 learns how to render that shared state visually. This is accomplished using a DiT-based world model conditioned directly on the shared game state, rather than prompts, images, or other traditional conditioning signals.You can think of this separation as loosely analogous to the structure of a modern game engine. The difference is that both components are entirely learned systems.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Odyssey.
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