Human intelligence emerged through the process of natural selection and evolution on Earth. We investigate what it would take to re-create this process in silico. While past work has often focused on low-level processes (such as simulating physics or chemistry), we instead take a more targeted approach, aiming to evolve agents that can accumulate open-ended culture and technologies across generations. Towards this, we present JaxLife: an artificial life simulator in which embodied agents, parameterized by deep neural networks, must learn to survive in an expressive world containing programmable systems. First, we describe the environment and show that it can facilitate meaningful Turing-complete computation. We then analyze the evolved emergent agents' behavior, such as rudimentary communication protocols, agriculture, and tool use. Finally, we investigate how complexity scales with the amount of compute used. We believe JaxLife takes a step towards studying evolved behavior in more open-ended simulations. Our code is available at https://github.com/luchris429/JaxLife

翻译：人类智能通过地球上的自然选择和进化过程而涌现。我们探讨了在计算机中重现这一过程所需的条件。以往的研究通常侧重于低层次过程（如模拟物理或化学），而我们则采取更具针对性的方法，旨在进化出能够跨世代积累开放式文化与技术的智能体。为此，我们提出了JaxLife：一种人工生命模拟器，其中由深度神经网络参数化的具身智能体必须学会在包含可编程系统的表达性世界中生存。首先，我们描述了该环境，并证明其能够支持有意义的图灵完备计算。随后，我们分析了进化涌现的智能体行为，例如原始通信协议、农业活动和工具使用。最后，我们探究了复杂度如何随计算资源规模而变化。我们相信JaxLife为在更开放的模拟环境中研究进化行为迈出了一步。代码发布于 https://github.com/luchris429/JaxLife