Multi-Agent Surveillance offers a flexible simulation framework where multiple AI agents act as predators or evaders in a discrete grid world. Users can configure environment parameters such as grid dimensions, number of agents, detection radii, and reward structures. The repository includes Python classes for agent behavior, scenario generation scripts, built-in visualization via matplotlib, and seamless integration with popular reinforcement learning libraries. This makes it easy to benchmark multi-agent coordination, develop custom surveillance strategies, and conduct reproducible experiments.

NeuralABM is an open-source Python library that leverages PyTorch to integrate neural networks into agent-based modeling. Users can specify agent architectures as neural modules, define environment dynamics, and train agent behaviors using backpropagation across simulation steps. The framework supports custom reward signals, curriculum learning, and synchronous or asynchronous updates, enabling the study of emergent phenomena. With utilities for logging, visualization, and dataset export, researchers and developers can analyze agent performance, debug models, and iterate on simulation designs. NeuralABM simplifies combining reinforcement learning with ABM for applications in social science, economics, robotics, and AI-driven game NPC behaviors. It provides modular components for environment customization, supports multi-agent interactions, and offers hooks for integrating external datasets or APIs for real-world simulations. The open design fosters reproducibility and collaboration through clear experiment configuration and version control integration.