jason-RL adds a reinforcement learning layer to the Jason multi-agent framework, allowing AgentSpeak BDI agents to learn action-selection policies via reward feedback. It implements Q-learning and SARSA algorithms, supports configuration of learning parameters (learning rate, discount factor, exploration strategy), and logs training metrics. By defining reward functions in agent plans and running simulations, developers can observe agents improve decision making over time, adapting to changing environments without manual policy coding.

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.

OpenMAS is designed to facilitate the development and evaluation of decentralized AI agents and multi-agent coordination strategies. It features a modular architecture that allows users to define custom agent behaviors, dynamic environment models, and inter-agent messaging protocols. The framework supports physics-based simulation, event-driven execution, and plugin integration for AI algorithms. Users can configure scenarios via YAML or Python, visualize agent interactions, and collect performance metrics through built-in analytics tools. OpenMAS streamlines prototyping in research areas such as swarm intelligence, cooperative robotics, and distributed decision-making.