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.

Archistar AI is a cutting-edge platform that integrates AI with architectural design to support property professionals in making informed decisions. It provides tools for rapidly assessing building permit compliance, visualizing 3D designs, and managing inventory efficiently. Archistar AI's capabilities span from site assessment to iterative design processes, enhancing productivity and ensuring compliance with regulations. The platform is particularly beneficial for discovering development sites and maximizing property value.