This example program was built on
- pysc2 (Deepmind) [https://github.com/deepmind/pysc2]
- baselines (OpenAI) [https://github.com/openai/baselines]
- s2client-proto (Blizzard) [https://github.com/Blizzard/s2client-proto]
- Tensorflow 1.3 (Google) [https://github.com/tensorflow/tensorflow]
- CollectMineralShards with Deep Q Network
The easiest way to get PySC2 is to use pip:
$ pip install git+https://github.com/deepmind/pysc2Also, you have to install baselines library.
$ pip install git+https://github.com/openai/baselinesYou have to purchase StarCraft II and install it. Or even the Starter Edition will work.
Follow Blizzard's documentation to
get the linux version. By default, PySC2 expects the game to live in
~/StarCraftII/.
Download the ladder maps
and the mini games
and extract them to your StarcraftII/Maps/ directory.
$ python train_mineral_shards.py --algorithm=a2c$ python enjoy_mineral_shards.py$ python train_mineral_shards.py --algorithm=deepq --prioritized=True --dueling=True --timesteps=2000000 --exploration_fraction=0.2$ python train_mineral_shards.py --algorithm=a2c --num_agents=2 --num_scripts=2 --timesteps=2000000| Description | Default | Parameter Type | |
|---|---|---|---|
| map | Gym Environment | CollectMineralShards | string |
| log | logging type : tensorboard, stdout | tensorboard | string |
| algorithm | Currently, support 2 algorithms : deepq, a2c | a2c | string |
| timesteps | Total training steps | 2000000 | int |
| exploration_fraction | exploration fraction | 0.5 | float |
| prioritized | Whether using prioritized replay for DQN | False | boolean |
| dueling | Whether using dueling network for DQN | False | boolean |
| lr | learning rate (if 0 set random e-5 ~ e-3) | 0.0005 | float |
| num_agents | number of agents for A2C | 4 | int |
| num_scripts | number of scripted agents for A2C | 4 | int |
| nsteps | number of steps for update policy | 20 | int |