This is the code for implementing the EDPC algorithm presented in the IEEE Conference on Decision and Control (CDC) 2023 paper.
- conda environment setting
conda create -n GT python=3.7 -y
conda activate GT
- nessary packages
pip install tensorflow==1.13.1
pip install protobuf==3.20
pip install numpy==1.16.5
pip install gym==0.13.0
pip install joblib imageio
python EPC/maddpg_o/experiments/train_epc1.py 2>&1 | tee epc_simplespread.txt
python EPC/maddpg_o/experiments/train_epc1.py --good-policy r-att-maddpg
We demonstrate here how the code can be used in conjunction with the(https://github.com/qian18long/epciclr2020/tree/master/mpe_local). It is based on(https://github.com/openai/multiagent-particle-envs)
@inproceedings{epciclr2020,
author = {Qian Long and Zihan Zhou and Abhinav Gupta and Fei Fang and Yi Wu and Xiaolong Wang},
title = {Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning},
booktitle = {International Conference on Learning Representations},
year = {2020}
}