PARL/examples/QMIX

• QMIX based on PARL and PaddlePaddle
• • QMIX
  • StarCraft2 Environment
• Benchmark Results
• How to Use
• • Dependencies
  • Start Training

QMIX based on PARL and PaddlePaddle

We reproduce the QMIX based on PARL and PaddlePaddle, reaching the same level of indicators as the paper in StarCraft2 benchmarks.

QMIX

QMIX is a value-based multi-agent reinforcement learning algorithm.
Learn more about QMIX from: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

StarCraft2 Environment

Paper: The StarCraft Multi-Agent Challenge
Github Repositories: smac

Benchmark Results

• We trained our model in 5 different scenarios: "3m", "8m", "2s_3z", "3s_5z" and "1c_3s_5z".
• The difficulty in all scenarios are set to be "7" (very difficult).
• We trained our model 3 times for each scenario.

How to Use

Dependencies

• python
• parl
• smac
• PaddlePaddle>=1.8.5

Start Training

1. Modify the config in qmix_config.py.
2. Start training:

   python train.py
   

3. View the training process with tensorboard:

   tensorboard --logdir ./