The theoretical properties of active inference agents are impressive, but how do we realize effective agents in working hardware and software on edge devices? This is an interesting problem because the computational load for policy exploration explodes exponentially, while the computational resources are very limited for edge devices. In this paper, we discuss the necessary features for a software toolbox that supports a competent non-expert engineer to develop working active inference agents. We introduce a toolbox-in-progress that aims to accelerate the democratization of active inference agents in a similar way as TensorFlow propelled applications of deep learning technology.

翻译：主动推理智能体的理论特性令人瞩目，但如何在边缘设备上通过可运作的硬件与软件实现高效智能体仍是一个值得探究的问题。由于策略探索的计算负载呈指数级增长，而边缘设备的计算资源极为有限，这构成了一个具有挑战性的课题。本文探讨了为支持具备一定能力的非专业工程师开发可运行的主动推理智能体，所需软件工具箱应具备的必要特性。我们介绍了一个正在开发中的工具箱原型，其目标是以类似TensorFlow推动深度学习技术应用的方式，加速主动推理智能体的普及化进程。