Fairness-in-Multi-Agent-Systems

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

? A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)

Multi-Agent Reinforcement Learning with JAX

A collection of MARL benchmarks based on TorchRL

POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.

This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.

A tool for aggregating and plotting MARL experiment data.

applying multi-agent reinforcement learning for highway-merging autonomous vehicles