We present SocialGym 2, a multi-agent navigation simulator for social robot research. Our simulator models multiple autonomous agents, replicating real-world dynamics in complex environments, including doorways, hallways, intersections, and roundabouts. Unlike traditional simulators that concentrate on single robots with basic kinematic constraints in open spaces, SocialGym 2 employs multi-agent reinforcement learning (MARL) to develop optimal navigation policies for multiple robots with diverse, dynamic constraints in complex environments. Built on the PettingZoo MARL library and Stable Baselines3 API, SocialGym 2 offers an accessible python interface that integrates with a navigation stack through ROS messaging. SocialGym 2 can be easily installed and is packaged in a docker container, and it provides the capability to swap and evaluate different MARL algorithms, as well as customize observation and reward functions. We also provide scripts to allow users to create their own environments and have conducted benchmarks using various social navigation algorithms, reporting a broad range of social navigation metrics. Projected hosted at: https://amrl.cs.utexas.edu/social_gym/index.html

翻译：我们提出SocialGym 2，一个面向社交机器人研究的多智能体导航模拟器。该模拟器对多个自主智能体进行建模，在包含门廊、走廊、交叉路口和环形交叉口的复杂环境中复现真实世界动力学。与专注于开放空间中具备基本运动学约束的单机器人的传统模拟器不同，SocialGym 2采用多智能体强化学习（MARL）来为具有多样化动态约束的多机器人在复杂环境中开发最优导航策略。基于PettingZoo MARL库和Stable Baselines3 API构建，SocialGym 2提供通过ROS消息传递与导航栈集成的易用Python接口。该模拟器可便捷安装并以Docker容器打包，支持切换和评估不同MARL算法，同时允许自定义观测与奖励函数。我们还提供脚本以支持用户创建自定义环境，并已使用多种社交导航算法进行基准测试，报告了涵盖广泛社交导航指标的评估结果。项目托管地址：https://amrl.cs.utexas.edu/social_gym/index.html