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StartCraft II Reinforcement Learning Examples

This example program was built on

Current examples

Minimaps

  • CollectMineralShards with Deep Q Network

CollectMineralShards

Quick Start Guide

1. Get PySC2

PyPI

The easiest way to get PySC2 is to use pip:

$ pip install git+https://github.com/deepmind/pysc2

Also, you have to install baselines library.

$ pip install git+https://github.com/openai/baselines

2. Install StarCraft II

Mac / Win

You have to purchase StarCraft II and install it. Or even the Starter Edition will work.

http://us.battle.net/sc2/en/legacy-of-the-void/

Linux Packages

Follow Blizzard's documentation to get the linux version. By default, PySC2 expects the game to live in ~/StarCraftII/.

3. Download Maps

Download the ladder maps and the mini games and extract them to your StarcraftII/Maps/ directory.

4. Train it!

$ python train_mineral_shards.py --algorithm=a2c

5. Enjoy it!

$ python enjoy_mineral_shards.py

4-1. Train it with DQN

$ python train_mineral_shards.py --algorithm=deepq --prioritized=True --dueling=True --timesteps=2000000 --exploration_fraction=0.2

4-2. Train it with A2C(A3C)

$ 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