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.

PommerLearn enables researchers and developers to train multi-agent RL bots in the Pommerman game environment. It includes ready-to-use implementations of popular algorithms (PPO, DQN), flexible configuration files for hyperparameters, automatic logging and visualization of training metrics, model checkpointing, and evaluation scripts. Its modular architecture makes it easy to extend with new algorithms, customize environments, and integrate with standard ML libraries such as PyTorch.