Patrolling-Zoo offers a flexible framework enabling users to create and experiment with multi-agent patrolling tasks in Python. The library includes a variety of grid-based and graph-based environments, each simulating surveillance, monitoring, and coverage scenarios. Users can configure the number of agents, map size, topology, reward functions, and observation spaces. Through compatibility with PettingZoo and Gym APIs, it supports seamless integration with popular reinforcement learning algorithms. This environment facilitates benchmarking and comparing MARL techniques under consistent settings. By providing standard scenarios and tools to customize new ones, Patrolling-Zoo accelerates research in autonomous robotics, security surveillance, search-and-rescue operations, and efficient area coverage using multi-agent coordination strategies.

Acuvity is designed to assist security administrators in discovering AI providers used within their organization. With this tool, Acuvity customers can deploy it to effectively monitor and manage various AI services. It provides detailed insights into available AI services, ensuring security standards and compliance are maintained across the organization. By using Acuvity, organizations can keep track of AI integrations and their usage, making it easier to secure their digital infrastructure and align with regulatory requirements.