README.md

StarCraft

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

Corresponding Papers

• IQL: Independent Q-Learning
• 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
• MAVEN: Multi-Agent Variational Exploration

Requirements

Use pip install -r requirements.txt to install the following requirements:

• matplotlib
• torch
• SMAC
• pysc2

Acknowledgement

• SMAC
• pymarl

TODO List

• Add CUDA option
• DyMA-CL
• G2ANet
• MAVEN
• VBC
• Other SOTA MARL algorithms
• Update results on other maps

Quick Start

$ python main.py --map=3m --alg=qmix

Directly run the main.py, then the algorithm will start training on map 3m. Note CommNet and G2ANet need an external training algorithm, so the name of them are like reinforce+commnet or central_v+g2anet, all the algorithms we provide are written in ./common/arguments.py.

If you just want to use this project for demonstration, you should set --evaluate=True --load_model=True.

The running of DyMA-CL is independent from others because it requires different environment settings, so we put it on another project. For more details, please read DyMA-CL documentation.

Result

We independently train these algorithms for 8 times and take the mean of the 8 independent results, and we evaluate them for 20 episodes every 100 training steps. All of the results are saved in ./result. Results on other maps are still in training, we will update them later.

1. Mean Win Rate of 8 Independent Runs on 3m --difficulty=7(VeryHard)

2. Mean Win Rate of 8 Independent Runs on 8m --difficulty=7(VeryHard)

3. Mean Win Rate of 8 Independent Runs on 2s3z --difficulty=7(VeryHard)

Replay

If you want to see the replay, make sure the replay_dir is an absolute path, which can be set in ./common/arguments.py. Then the replays of each evaluation will be saved, you can find them in your path.