# SAC-QMIX **Repository Path**: majingself/SAC-QMIX ## Basic Information - **Project Name**: SAC-QMIX - **Description**: No description available - **Primary Language**: Unknown - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-12-05 - **Last Updated**: 2023-12-05 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # SAC-QMIX Algorithm that applies SAC to QMIX for Multi-Agent Reinforcement Learning. Watch the [demo](https://youtu.be/T0t-d1e7IkE) here. ## Requirements SMAC pytorch (GPU support recommanded while training) tensorboard StarCraft II For the installation of SMAC and StarCraft II, refer to the repository of [SMAC](https://github.com/oxwhirl/smac). ## Train Train a model with the following command: ```shell python main.py ``` Configurations and parameters of the training are specified in `config.json`. Models will be saved at `./models` ## Test Test a trained model with the following command: ```shell python test_model.py ``` Configurations and parameters of the testing are specified in `test_config.json`. Match the `run_name` items in `config.json` and `test_config.json`. ## Theory & Algorithm ### Architecture
Q-values computed by networks:
Individual state-value functions:
Total state-values (alpha is the entropy temperature):
Q-values expressed with Bellman Function:
Critic networks update: minimum
Actor networks update: maximum
Entropy temperatures update: minimum
## Result
Note that data of other algorithm are from [SMAC paper](https://github.com/oxwhirl/smac/releases/download/v1/smac_run_data.json). Therefore methods of evaluations are kept the same as [SMAC paper](https://arxiv.org/abs/1902.04043) did (StarCraftII version: SC2.4.6.2.69232).
### Test Win Rate % of SAC-QMIX and other algorithms
(Mean of 5 independent runs)