RinSim provides a modular simulation environment focused on modeling dynamic logistics scenarios with multiple autonomous agents. Users can define road networks via graph structures, configure fleets of vehicles including electric models with battery constraints, and simulate stochastic request arrivals for pickup and delivery tasks. The discrete-event architecture ensures precise timing and event management, while built-in routing algorithms and customizable agent behaviors allow extensive experimentation. RinSim supports output metrics such as travel time, energy consumption, and service level, and includes visualization modules for real-time and post-simulation analysis. Its extensible design enables integration of custom algorithms, scaling up to large fleets, and reproducible research workflows essential for academia and industry optimization of mobility strategies.

VacuumWorld is an open-source simulation platform designed to facilitate the development and evaluation of multi-agent reinforcement learning algorithms. It provides grid-based environments where virtual vacuum cleaner agents operate to detect and remove dirt patches across customizable layouts. Users can adjust parameters such as grid size, dirt distribution, stochastic movement noise, and reward structures to model diverse scenarios. The framework includes built-in support for agent communication protocols, real-time visualization dashboards, and logging utilities for performance tracking. With simple Python APIs, researchers can quickly integrate their RL algorithms, compare cooperative or competitive strategies, and conduct reproducible experiments, making VacuumWorld ideal for academic research and teaching.