CrewAI-Learning is an open-source library designed to streamline multi-agent reinforcement learning projects. It offers environment scaffolding, modular agent definitions, customizable reward functions, and a suite of built-in algorithms such as DQN, PPO, and A3C adapted for collaborative tasks. Users can define scenarios, manage training loops, log metrics, and visualize results. The framework supports dynamic configuration of agent teams and reward sharing strategies, making it easy to prototype, evaluate, and optimize cooperative AI solutions across various domains.

MAPF_G2RL is an open-source research framework that bridges graph theory and deep reinforcement learning to tackle the multi-agent path finding (MAPF) problem. It encodes nodes and edges into vector representations, defines spatial and collision-aware reward functions, and supports various RL algorithms such as DQN, PPO, and A2C. The framework automates scenario creation by generating random graphs or importing real-world maps, and orchestrates training loops that optimize policies for multiple agents simultaneously. After learning, agents are evaluated in simulated environments to measure path optimality, makespan, and success rates. Its modular design allows researchers to extend core components, integrate new MARL techniques, and benchmark against classical solvers.