CybMASDE enables researchers and developers to build, configure, and execute multi-agent simulations with deep reinforcement learning. Users can author custom scenarios, define agent roles and reward functions, and plug in standard or custom RL algorithms. The framework includes environment servers, networked agent interfaces, data collectors, and rendering utilities. It supports parallel training, real-time monitoring, and model checkpointing. CybMASDE’s modular architecture allows seamless integration of new agents, observation spaces, and training strategies, accelerating experimentation in cooperative control, swarm behavior, resource allocation, and other multi-agent use cases.

MineLand provides a flexible 3D sandbox environment inspired by Minecraft for training reinforcement learning agents. It features Gym-compatible APIs for seamless integration with existing RL libraries such as Stable Baselines, RLlib, and custom implementations. Users gain access to a library of tasks, including resource collection, navigation, and construction challenges, each with configurable difficulty and reward structures. Real-time rendering, multi-agent scenarios, and headless modes allow for scalable training and benchmarking. Developers can design new maps, define custom reward functions, and plugin additional sensors or controls. MineLand’s open-source codebase fosters reproducible research, collaborative development, and rapid prototyping of AI agents in complex virtual worlds.

Multi Agent Simulation offers a flexible API to define Agent classes with custom sensors, actuators, and decision logic. Users configure environments with obstacles, resources, and communication protocols, then run step-based or real-time simulation loops. Built-in logging, event scheduling, and Matplotlib integration help track agent states and visualize results. The modular design allows easy extension with new behaviors, environments, and performance optimizations, making it ideal for academic research, educational purposes, and prototyping multi-agent scenarios.

This open-source framework combines the AgentSimJs agent modeling library with Three.js's 3D graphics engine to deliver interactive, browser-based multi-agent simulations. Users can define agent types, behaviors, and environmental rules, configure collision detection and event handling, and visualize simulations in real time with customizable rendering options. The library supports dynamic controls, scene management, and performance tuning, making it ideal for research, education, and prototyping of complex agent-based scenarios.

AgentSimulation is an open-source Python library built on Pygame for simulating multiple autonomous agents in a 2D environment. It allows users to configure agent properties, steering behaviors (seek, flee, wander), collision detection, pathfinding, and interactive rules. With real-time rendering and modular design, it supports rapid prototyping, teaching simulations, and small-scale research in swarm intelligence or multi-agent interactions.