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https://github.com/liuruoze/HierNet-SC2
(AAAI'2019) The codes, models, logs, and data for an extended paper of the original paper "On Reinforcement Learning for Full-length Game of StarCraft". AAAI = AAAI Conference on Artificial Intelligence.
https://github.com/liuruoze/HierNet-SC2
deep-learning deep-neural-networks deep-reinforcement-learning hierarchical-reinforcement-learning reinforcement-learning starcraft-ii starcraft2 starcraft2-agents starcraft2-ai
Last synced: 2 months ago
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(AAAI'2019) The codes, models, logs, and data for an extended paper of the original paper "On Reinforcement Learning for Full-length Game of StarCraft". AAAI = AAAI Conference on Artificial Intelligence.
- Host: GitHub
- URL: https://github.com/liuruoze/HierNet-SC2
- Owner: liuruoze
- License: apache-2.0
- Created: 2021-12-24T10:29:45.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-05T01:30:33.000Z (almost 2 years ago)
- Last Synced: 2024-07-12T20:10:43.806Z (3 months ago)
- Topics: deep-learning, deep-neural-networks, deep-reinforcement-learning, hierarchical-reinforcement-learning, reinforcement-learning, starcraft-ii, starcraft2, starcraft2-agents, starcraft2-ai
- Language: Python
- Homepage:
- Size: 1.14 MB
- Stars: 21
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-sc2-ai - HierNet-SC2
README
# HierNet-SC2
This repository contains the codes, models, training logs, and data for an extended paper of the original conference paper on AAAI 2019, "On Reinforcement Learning for Full-length Game of StarCraft".
The extended journal paper is named "On Efficient Reinforcement Learning for Full-length Game of StarCraft II", and the arxiv link is [here](https://arxiv.org/abs/2209.11553).
## Codes
The below table shows the codes in the project. We provide codes for the training of agents, generating replays, and mining macro actions.
Content | Description
------------ | -------------
./main.py | the codes for the training code
./mine_from_replay.py | the codes for mining macro-actions
./play_for_replay.py | the codes for generating replays
./multi_agent.py | the codes for the agent
./new_network.py | the codes for the neural network model
./param.py | the hyper-parameters
./algo/ | the algorithm of ppo
./lib/ | the library functions
## Data
The below table shows the data for training agent and generating macro actions.
Content | Description
------------ | -------------
./data/replay/ | 30 replays played by the expert for generating macro actions
./data/replay_data/ | using data mining algorithms to mine macro actions from the above replays
## Logs
The below table shows the logs of training on cheating level built-in AIs.
Content | Description
------------ | -------------
./logs/lv10-0.20_to_0.90/ | the training log in lv-10 which takes the win-rate from 0.20 to 0.90
./logs/lv10-0.90_to_0.94/ | the training log in lv-10 which takes the win-rate from 0.90 to 0.94
./logs/lv8-0_to_0.960/ | the training log in lv-8 which takes the win-rate to 0.960, restore=lv10-0.20_to_0.90
./logs/lv9-0_to_0.967/ | the training log in lv-9 which takes the win-rate to 0.967, restore=lv10-0.20_to_0.90
## Model
The below table shows the model of a 0.94 win rate in lv-10.
Content | Description
------------ | -------------
./model/lv10-0.94/ | the model which gets win-rate 0.94 in lv-10
## Requirements
- python==3.5
- tensorflow==1.5
- future==0.18.2
- pysc2==1.2
- matplotlib==3.3.4
- scipy==1.1.0
- prefixspan==0.5.2
## Install
A simple straightway is, you can first use conda like:
```
conda create -n tf_1_5 python=3.5 tensorflow-gpu=1.5
```
to install Tensorflow-gpu 1.5 (with accompanied CUDA and cudnn).
Next, you should activate the conda environment, like:
```
conda activate tf_1_5
```
Then you can install other python packages by pip, e.g., the command is:
```
pip install -r requirements.txt
```
## Usage
Run main.py to train an agent against the most difficult built-in bot (lv-10) in StarCraft II.
Run mine_from_replay.py to mine macro actions from replays.
Run play_for_replay.py to generate replays by your selves.