This is a simple implementation of DeepMind's PySC2 RL agents. In
this project, the agents are defined according to the original paper, which use all feature maps and structured information to predict both actions and arguments via an A3C algorithm.
Requirements
PySC2 is a learning environment of StarCraft II provided by
DeepMind. It provides an interface for RL agents to interact with
StarCraft II, getting observations and sending actions. You can follow
the tutorial in PySC2 repo to install it.
Download the pretrained model from here and extract them to ./snapshot/.
Test the pretrained model:
python -m main --map=MoveToBeacon --training=False
You will get the following results for different maps.
MoveToBeacon
CollectMineralShards
DefeatRoaches
Mean Score
~25
~62
~87
Max Score
31
97
371
Training
Train a model by yourself:
python -m main --map=MoveToBeacon
Notations
Different from the original A3C algorithm, we replace the policy penalty term with epsilon greedy exploration.
When train a model by yourself, you'd better to run several times
and choose the best one. If you get better results than ours, it's
grateful to share with us.