Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL agents. We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents. The package is designed for training a population of agents in multi-agent environments and evaluating their ability to generalize to diverse background agents. It is built on top of DeepMind's JAX ecosystem~\cite{deepmind2020jax} and leverages the RL ecosystem developed by DeepMind. Our framework marl-jax is capable of working in cooperative and competitive, simultaneous-acting environments with multiple agents. The package offers an intuitive and user-friendly command-line interface for training a population and evaluating its generalization capabilities. In conclusion, marl-jax provides a valuable resource for researchers interested in exploring social generalization in the context of MARL. The open-source code for marl-jax is available at: \href{https://github.com/kinalmehta/marl-jax}{https://github.com/kinalmehta/marl-jax}

翻译：强化学习（RL）的最新进展催生了许多令人瞩目的应用。这些进步得益于算法与工程的双重改进，从而实现了RL智能体的更快训练。我们提出marl-jax，这是一个用于训练和评估智能体社会泛化能力的多智能体强化学习软件包。该软件包旨在多智能体环境中训练智能体种群，并评估其对多样化背景智能体的泛化能力。它基于DeepMind的JAX生态系统~\cite{deepmind2020jax}构建，并利用了DeepMind开发的RL生态系统。我们的框架marl-jax能够支持包含多个智能体的协作式与竞争式同步交互环境。该软件包提供了一个直观且用户友好的命令行界面，用于训练种群并评估其泛化能力。总之，marl-jax为在MARL背景下探索社会泛化问题的研究人员提供了宝贵资源。marl-jax的开源代码可访问：\href{https://github.com/kinalmehta/marl-jax}{https://github.com/kinalmehta/marl-jax}