NKC Multi-Agent Models provides researchers and developers with a comprehensive toolkit for designing, training, and evaluating multi-agent reinforcement learning systems. It features a modular architecture where users define custom agent policies, environment dynamics, and reward structures. Seamless integration with OpenAI Gym allows for rapid prototyping, while support for TensorFlow and PyTorch enables flexibility in selecting learning backends. The framework includes utilities for experience replay, centralized training with decentralized execution, and distributed training across multiple GPUs. Extensive logging and visualization modules capture performance metrics, facilitating benchmarking and hyperparameter tuning. By simplifying the setup of cooperative, competitive, and mixed-motive scenarios, NKC Multi-Agent Models accelerates experimentation in domains such as autonomous vehicles, robotic swarms, and game AI.

Aeiva is a developer-first platform that enables you to create, deploy, and evaluate autonomous AI agents within flexible simulation environments. It features a plugin-based engine for environment definition, intuitive APIs to customize agent decision loops, and built-in metrics collection for performance analysis. The framework supports integration with OpenAI Gym, PyTorch, and TensorFlow, plus real-time web UI for monitoring live simulations. Aeiva’s benchmarking tools let you organize agent tournaments, record results, and visualize agent behaviors to fine-tune strategies and accelerate multi-agent AI research.

Shepherding is an open-source simulation framework designed for reinforcement learning researchers and developers to study and implement multi-agent herding tasks. It provides a Gym-compatible environment where agents can be trained to perform behaviors such as flanking, collecting, and dispersing target groups across continuous or discrete spaces. The framework includes modular reward shaping functions, environment parameterization, and logging utilities for monitoring training performance. Users can define obstacles, dynamic agent populations, and custom policies using TensorFlow or PyTorch. Visualization scripts generate trajectory plots and video recordings of agent interactions. Shepherding’s modular design allows seamless integration with existing RL libraries, enabling reproducible experiments, benchmarking of novel coordination strategies, and rapid prototyping of AI-driven herding solutions.