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.

Vanilla Agents is a lightweight PyTorch-based framework that delivers modular and extensible implementations of core reinforcement learning agents. It supports algorithms like DQN, Double DQN, PPO, and A2C, with pluggable environment wrappers compatible with OpenAI Gym. Users can configure hyperparameters, log training metrics, save checkpoints, and visualize learning curves. The codebase is organized for clarity, making it ideal for research prototyping, educational use, and benchmarking new ideas in RL.

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.

The MARL Simulator is designed to facilitate efficient and scalable development of multi-agent reinforcement learning (MARL) algorithms. Leveraging PyTorch's distributed backend, it allows users to run parallel training across multiple GPUs or nodes, significantly reducing experiment runtime. The simulator offers a modular environment interface that supports standard benchmark scenarios—such as cooperative navigation, predator-prey, and grid world—as well as user-defined custom environments. Agents can utilize various communication protocols to coordinate actions, share observations, and synchronize rewards. Configurable reward and observation spaces enable fine-grained control over training dynamics, while built-in logging and visualization tools provide real-time insights into performance metrics.

This open-source project delivers a complete multi-agent reinforcement learning framework built on PyTorch and Unity ML-Agents. It offers decentralized DDPG algorithms, environment wrappers, and training scripts. Users can configure agent policies, critic networks, replay buffers, and parallel training workers. Logging hooks allow TensorBoard monitoring, while modular code supports custom reward functions and environment parameters. The repository includes sample Unity scenes demonstrating collaborative navigation tasks, making it ideal for extending and benchmarking multi-agent scenarios in simulation.

This repository implements emergent communication in multi-agent reinforcement learning using PyTorch. Users can configure sender and receiver neural networks to play referential games or cooperative navigation, encouraging agents to develop a discrete or continuous communication channel. It offers scripts for training, evaluation, and visualization of learned protocols, along with utilities for environment creation, message encoding, and decoding. Researchers can extend it with custom tasks, modify network architectures, and analyze protocol efficiency, fostering rapid experimentation in emergent agent communication.

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.