Foragax：基于JAX的智能体建模框架
Foragax: An Agent-Based Modelling Framework Based on JAX
AI总结：
本文提出Foragax，一个基于JAX的硬件加速多智能体觅食模拟框架，支持端到端向量化与可微分模拟，可扩展至数千智能体，并适用于多种多智能体场景。
摘要：
在共享环境中寻找资源是生物体维持体内稳态所进行的普遍活动。在计算机上模拟多智能体觅食，使我们能够以可处理的方式研究个体和集体的涌现行为。基于智能体的建模已被证明在模拟此类任务方面是有效的，尽管将模拟扩展到容纳大量具有复杂动态的智能体仍然具有挑战性。在这项工作中，我们提出了Foragax，一个通用、可扩展、硬件加速的多智能体觅食工具包。利用JAX库，我们的工具包可以以端到端向量化和可微分的方式，模拟数千个智能体在共同环境中觅食。该工具包提供了基于智能体的建模工具，用于模拟各种觅食任务，包括设计自定义空间和时间智能体动态、控制策略、传感器模型和边界条件的选项。此外，此类模拟中的智能体数量可以根据自定义规则增加或减少。虽然应用于觅食，该工具包也可用于建模和模拟各种其他多智能体场景。
英文摘要：
Foraging for resources is a ubiquitous activity conducted by living organisms in a shared environment to maintain their homeostasis. Modelling multi-agent foraging in-silico allows us to study both individual and collective emergent behaviour in a tractable manner. Agent-based modelling has proven to be effective in simulating such tasks, though scaling the simulations to accommodate large numbers of agents with complex dynamics remains challenging. In this work, we present Foragax, a general-purpose, scalable, hardware-accelerated, multi-agent foraging toolkit. Leveraging the JAX library, our toolkit can simulate thousands of agents foraging in a common environment, in an end-to-end vectorized and differentiable manner. The toolkit provides agent-based modelling tools to model various foraging tasks, including options to design custom spatial and temporal agent dynamics, control policies, sensor models, and boundary conditions. Further, the number of agents during such simulations can be increased or decreased based on custom rules. While applied to foraging, the toolkit can also be used to model and simulate a wide range of other multi-agent scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
