The Multi-Agent Autonomous Waste Collection System is a research-driven platform that employs multi-agent reinforcement learning to train individual waste-collecting robots to collaborate on route planning. Agents learn to avoid redundant coverage, minimize travel distance, and respond to dynamic waste generation patterns. Built in Python, the system integrates a simulation environment for testing and refining policies before real-world deployment. Users can configure map layouts, waste drop-off points, agent sensors, and reward structures to tailor behavior to specific urban areas or operational constraints.

OutSpeed is an advanced AI-powered platform designed to optimize fleet management. By leveraging real-time data and analytics, OutSpeed provides a comprehensive solution to enhance operational efficiency, reduce operational costs, and improve overall fleet performance. The platform is robust and scalable, making it suitable for managing fleets of any size. OutSpeed's intelligent algorithms analyze various metrics such as route efficiency, fuel consumption, and vehicle maintenance needs to deliver actionable insights, ensuring that fleet operations are always running at their best.