As this work is based on "DanZero: Mastering GuanDan Game with Reinforcement Learning", the code is also built on the repository "https://github.com/AltmanD/guandan_mcc/tree/main".
lib needs(If you just train the DMC model, torch is not required):
linux20.04
python=3.8(learner) python=3.6(actor)
tensorflow=1.15.5+nv22.2(learner) or 1.15.4(actor)
numpy=1.18.5
websocket(ws4py)=0.5.1
pyarrow=5.0.0
pyzmq=22.3.0
torch=1.9.1+cpu(actor) or 1.13.1+cu116(learner)
To realize the communication between dockers, you can refer to https://cloud.tencent.com/developer/article/1013167. If you use the docker, follow the create_containeder.sh to set docker network. Then you can enter the learner and use "ssh-keygen -t rsa" to create the pub file and copy this to authorized_keys file in the actors. After that, edit the /etc/ssh/ssh_config file to set "StrictHostKeyChecking" to be no. In this way, the dockers can communicate directly.
For convenience of installation, we offer the actor image at Link：https://pan.baidu.com/s/1ICAKWF3F-LxraphzqNjYhg?pwd=0704 Extracted code：0704
The direct command to run the code is as below:
actor: python actor_n/actor.py
learner: python learner_n/learner.py
Here we offer a start shell file in the learner directory.
The evaluation code is in the ./wintest directory and we give introduction in the directory.