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https://github.com/chris-chris/pysc2-examples
StarCraft II - pysc2 Deep Reinforcement Learning Examples
https://github.com/chris-chris/pysc2-examples
ai deep-q-network deep-reinforcement-learning deepmind machine-learning reinforcement-learning startcraft2
Last synced: 3 months ago
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StarCraft II - pysc2 Deep Reinforcement Learning Examples
- Host: GitHub
- URL: https://github.com/chris-chris/pysc2-examples
- Owner: chris-chris
- License: apache-2.0
- Created: 2017-08-19T14:50:06.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-03-03T00:40:33.000Z (over 3 years ago)
- Last Synced: 2024-04-05T04:35:18.265Z (7 months ago)
- Topics: ai, deep-q-network, deep-reinforcement-learning, deepmind, machine-learning, reinforcement-learning, startcraft2
- Language: Python
- Homepage:
- Size: 4.96 MB
- Stars: 754
- Watchers: 37
- Forks: 357
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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:
```shell
$ pip install git+https://github.com/deepmind/pysc2
```
Also, you have to install `baselines` library.
```shell
$ 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](https://github.com/Blizzard/s2client-proto#downloads) to
get the linux version. By default, PySC2 expects the game to live in
`~/StarCraftII/`.
* [3.16.1](http://blzdistsc2-a.akamaihd.net/Linux/SC2.3.16.1.zip)
## 3. Download Maps
Download the [ladder maps](https://github.com/Blizzard/s2client-proto#downloads)
and the [mini games](https://github.com/deepmind/pysc2/releases/download/v1.2/mini_games.zip)
and extract them to your `StarcraftII/Maps/` directory.
## 4. Train it!
```shell
$ python train_mineral_shards.py --algorithm=a2c
```
## 5. Enjoy it!
```shell
$ python enjoy_mineral_shards.py
```
## 4-1. Train it with DQN
```shell
$ python train_mineral_shards.py --algorithm=deepq --prioritized=True --dueling=True --timesteps=2000000 --exploration_fraction=0.2
```
## 4-2. Train it with A2C(A3C)
```shell
$ 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 |