AgentGarten: a code world framework for continually evolving agents
AgentGarten: Code Worlds for Evolving Agents
The paper proposes the AgentGarten framework, which couples simulators and game engines to a shared neural renderer to build real-time interactive virtual environments. The simulation backend maintains persistent world state and executes program-defined interaction rules, while the renderer generates visual observations from structured conditions exported through a unified interface; the neural renderer is adapted from a pretrained video model to take geometry-conditioned input, is distilled with the proposed Adversarial Forcing, and has its inference optimized for real-time interaction.
The paper wires simulators and game engines into a single neural renderer, and reports learning-efficiency results comparing an agent's 4 rounds of experience with millions of rounds of reinforcement learning.
Source: arXiv Game AI · arxiv.org