Code for the paper "Coordinating Multi-Agent Reinforcement Learning via Dual Collaborative Constraints" submitted to Neural Networks.
This repository develops DCC algorithm on both Multi-agent Particle Environments and StarCraft Multi-Agent Challenge benchmarks, and compares it with several baselines including LDSA, MAVEN, ROMA, QMIX, HSD and RODE_nr.
To install requirements:
pip install -r requirements.txt
To train the approach in the paper, run this command:
python main.py
You can select the training task between MPE and SMAC by setting --env-config='sc2' or 'mpe'
.
Also you can select the training algorithm by setting --config='dcc'
To modify the hyper-parameters of algorithms and environments, refer to:
src/config/algs/dcc.yaml
src/config/default.yaml
src/config/envs/mpe.yaml
src/config/envs/sc2.yaml
This repository is developed based on PyMARL. And we have cited the SMAC paper in our work.