common packages
Python3 is required to resolve multiprocessing issue.
Detail installation steps.
<project_path>
: full path of this repo in local environment<hose_name>
: host name of local environment (ex. localhost
)
To train agent in "FindAndDefeatZerglings" mini game with 8 different worker threads:
cd <project_path>
python -m rl.main --map-name FindAndDefeatZerglings --num-processes 8
Use python rl/main.py --help
to see all available options.
To train with GPU, use --gpu_ids
option. Default is CPU only.
# Use two GPUs for example
python -m rl.main --map-name FindAndDefeatZerglings --gpu_ids 0 1 --num-processes 8
To visualize training progress stats, run Tensorboard (tensorflow required).
tensorboard --logdir <project_path>/output/summaries
Then open the link http://
All output files are located in <project_path>/output
by default.
<project_path>/output/models
<project_path>/output/logs
<project_path>/output/summaries
Follow instruction here and chose OS, Package Manager, Python version and CUDA version accordingly.
# check cuda version
nvcc --version
# use CUDA 9.1 as example
pip3 install http://download.pytorch.org/whl/cu91/torch-0.4.0-cp35-cp35m-linux_x86_64.whl
# no GPU as example
pip3 install torch
pip install pysc2
pip install git+https://github.com/lanpa/tensorboard-pytorch
# TensorFlow is required for dashboard visualization
pip install tensorflow
pytorch reinforcement learning
pysc2 integratioin