Mean-Field MARL provides a robust Python framework for implementing and evaluating mean-field multi-agent reinforcement learning algorithms. It approximates large-scale agent interactions by modeling the average effect of neighboring agents via mean-field Q-learning. The library includes environment wrappers, agent policy modules, training loops, and evaluation metrics, enabling scalable training across hundreds of agents. Built on PyTorch for GPU acceleration, it supports customizable environments like Particle World and Gridworld. Modular design allows easy extension with new algorithms, while built-in logging and Matplotlib-based visualization tools track rewards, loss curves, and mean-field distributions. Example scripts and documentation guide users through setup, experiment configuration, and result analysis, making it ideal for both research and prototyping of large-scale multi-agent systems.

RL-Agents is a research-grade reinforcement learning framework built on PyTorch that bundles popular RL algorithms across value-based, policy-based, and actor-critic methods. The library features a modular agent API, GPU acceleration, seamless integration with OpenAI Gym, and built-in logging and visualization tools. Users can configure hyperparameters, customize training loops, and benchmark performance with a few lines of code, making RL-Agents ideal for academic research, prototyping, and industrial experimentation.

Emergent Communication in Agents is an open-source PyTorch framework designed for researchers exploring how multi-agent systems develop their own communication protocols. The library offers flexible implementations of cooperative reinforcement learning tasks, including referential games, combination games, and object identification challenges. Users define speaker and listener agent architectures, specify message channel properties like vocabulary size and sequence length, and select training strategies such as policy gradients or supervised learning. The framework includes end-to-end scripts for running experiments, analyzing communication efficiency, and visualizing emergent languages. Its modular design allows easy extension with new game environments or custom loss functions. Researchers can reproduce published studies, benchmark new algorithms, and probe compositionality and semantics of emergent agent languages.