JasonEnvironments delivers a collection of environment modules designed specifically for the Jason multi-agent system. Each module exposes a standardized interface so agents can perceive, act, and interact within diverse scenarios like pursuit-evasion, resource foraging, and cooperative tasks. The library is easy to integrate into existing Jason projects: just include the JAR, configure the desired environment in your agent architecture file, and launch the simulation. Developers can also extend or customize parameters and rules to tailor the environment to their research or educational needs.

Multi-Agent Inspection Simulation provides a comprehensive framework for simulating and training multiple autonomous agents to perform inspection tasks cooperatively within Unity 3D environments. It integrates with the Unity ML-Agents toolkit, offering configurable scenes with inspection targets, adjustable reward functions, and agent behavior parameters. Researchers can script custom environments, define the number of agents, and set training curricula via Python APIs. The package supports parallel training sessions, TensorBoard logging, and customizable observations including raycasts, camera feeds, and positional data. By adjusting hyperparameters and environment complexity, users can benchmark reinforcement learning algorithms on coverage, efficiency, and coordination metrics. The open-source codebase encourages extension for robotics prototyping, cooperative AI research, and educational demonstrations in multi-agent systems.