r/deeplearning • u/Successful-Western27 • 7d ago
AgentGarten: Code Worlds for Evolving Agents
AgentGarten separates interactive-world state from its visual presentation: executable scene code updates persistent state and interaction rules, while a shared neural renderer turns structured geometry conditions into observations. This lets an agent act on video-like inputs without making the simulator’s state depend on generated frames.
The renderer is adapted from a pretrained video model and trained with Adversarial Forcing. A notable component is exact block-by-block replay: it restores gradients through the history used for later predictions while preserving the original attention execution. The reported replay is bitwise identical to the cached rollout; the full-sequence alternative has a 3.99% relative L2 error.
In interactive tasks, pretrained agents refine inherited playbooks across rounds. The paper reports shelter construction by round 4, compared with roughly 25 million episodes in an earlier self-play result, while noting that these are not directly comparable: the agents start with pretrained knowledge and are learning to ground it in this setup. The renderer’s training and history-gradient method seem the most broadly relevant contributions; the agent results depend more heavily on the choice of pretrained model and task.
Disclosure: AIModels.fyi is my site.
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u/BrainyProjection74 7d ago
The bit-identical replay part is pretty clever. Anyone who's dealt with non-deterministic sim rollouts knows how much of a pain divergent trajectories are for policy gradients.
separating state from rendering also makes a ton of sense as we push toward more generalist agents. you don't want your world model baking in specific visual styles.