Multi-Agent Drone Environment is a Python package offering a customizable multi-agent simulation for UAV swarms, built on OpenAI Gym and PyBullet. Users define multiple drone agents with kinematic and dynamic models to explore cooperative tasks such as formation flying, target tracking, and obstacle avoidance. The environment supports modular task configuration, realistic collision detection, and sensor emulation, while allowing custom reward functions and decentralized policies. Developers can integrate their own reinforcement learning algorithms, evaluate performance under varied scenarios, and visualize agent trajectories and metrics in real time. Its open-source design encourages community contributions, making it ideal for research, teaching, and prototyping advanced multi-agent control solutions.

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.