StarCraft

This is a pytorch implementation of the multi-agent reinforcement learning algorithms, including QMIX, VDN, COMA, QTRAN(both QTRAN-base and QTRAN-alt), CommNet, DyMA-CL, and G2ANet, which are the state of art MARL algorithms. In addition, because CommNet and G2ANet need a external training algorithm, you can combine them with COMA, we also provide Central-V and REINFORCE for them to training. We trained these algorithms on SMAC, the decentralised micromanagement scenario of StarCraft II.

Corresponding Papers

• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

• Value-Decomposition Networks For Cooperative Multi-Agent Learning

• Counterfactual Multi-Agent Policy Gradients

• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

• Learning Multiagent Communication with Backpropagation

• From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

• Multi-Agent Game Abstraction via Graph Attention Neural Network

github地址：https://github.com/starry-sky6688/StarCraft