zhengpeng-guo/pysc2-examples

StarCraft II - pysc2 Deep Reinforcement Learning Examples

StartCraft II Reinforcement Learning Examples

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]

Current examples

Minimaps

• CollectMineralShards with Deep Q Network

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/.

• 3.16.1

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