MSC: A Dataset for Macro-Management in StarCraft II
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README.md

MSC

MSC: A Dataset for Macro-Management in StarCraft II.

@article{wu2017msc,
  title={MSC: A Dataset for Macro-Management in StarCraft II},
  author={Wu, Huikai and Zhang, Junge and Huang, Kaiqi},
  journal={arXiv preprint arXiv:1710.03131},
  year={2017}
}

Download

FTP: ftp://surveillance.idealtest.org/ (User Name: msc; Password: msc)

  • [Stat]: ftp://msc:msc@surveillance.idealtest.org/Stat.tar.gz

  • [TRAIN|VAL|TEST]: ftp://msc:msc@surveillance.idealtest.org/TRAIN-VAL-TEST.tar.gz

  • [Global]: ftp://msc:msc@surveillance.idealtest.org/GlobalFeatureVector.tar.gz

  • [Spatial]:

    • TvT: ftp://msc:msc@surveillance.idealtest.org/TvT.tar.gz
    • TvP: ftp://msc:msc@surveillance.idealtest.org/PvT.tar.gz
    • TvZ: ftp://msc:msc@surveillance.idealtest.org/TvZ/TvZ.tar.gz
    • PvP: ftp://msc:msc@surveillance.idealtest.org/PvP.tar.gz
    • PvZ: ftp://msc:msc@surveillance.idealtest.org/PvZ.tar.gz
    • ZvZ: ftp://msc:msc@surveillance.idealtest.org/ZvZ.tar.gz

For Linux and MacOS:

wget ftp://msc:msc@surveillance.idealtest.org/[FILE_NAME]

NOTE:

  • Global features are also available HERE.
  • [TRAIN|VAL|TEST] split is also available HERE.
  • [Stat] is also available HERE. The stat files with postfix _human.json are human-readable.

Baselines

Global State Evaluation

Method TvT:T TvZ:T TvZ:Z TvP:T TvP:P ZvZ:Z ZvP:Z ZvP:P PvP:P
Baseline[Global] 61.09 58.89 60.61 57.21 60.95 59.91 59.95 59.35 51.36
Baseline[Spatial] 50.85 52.35 59.82 54.90 59.15 54.65 55.02 58.78 57.76

Build Order Prediction

Method TvT:T TvZ:T TvZ:Z TvP:T TvP:P ZvZ:Z ZvP:Z ZvP:P PvP:P
Baseline[Global] 74.12 73.01 73.89 70.29 79.28 76.07 72.02 78.08 76.28
Baseline[Spatial] 73.07 73.71 75.92 64.15 75.09 74.88 72.32 76.12 74.22

Dataset: Global Feature Vector

Each replay is a (T, M) matrix F, where F[t, :] is the feature vector for time step t.

Each row of F is a M-dimensional vector, with M varying as [RACE] v.s. [RACE].

The M-dimensional vector is orgnized as follows:

  1. [0]: reward, i.e. final result of the game. 0: DEFEAT, 1: WIN.
  2. [1]: ground truth action, ranging from [0, #ACTION].
  3. [2-15): cumulative score [NOT NORMALIZED], which is defined in Here.
  4. [15-M): observation feature vector, which is normalized into [0, 1].
    1. [15]: frame id.

    2. [16-27): player info, including various resources and n_power_source.

    3. [27-#1): alerts, boolean.

    4. [#1-#2): upgrades, boolean.

    5. [#2-#3): research count.

    6. [#3-#4): friendly units info, which is defined in Here.

    7. [#4-M): enemy units info, where M = #4 + #[ENEMY RACE].

      V.S. TvT:T TvZ:T TvZ:Z TvP:T TvP:P ZvZ:Z ZvP:Z ZvP:P PvP:P
      M 753 1131 1121 663 653 1499 1031 1031 563
      RACE #1 #2 #3 #4 #ACTION #RACE
      Terran 29 60 81 417 75 336
      Protoss 29 55 71 317 61 246
      Zerg 29 55 71 785 74 714

Code for loading F:

import numpy as np
from scipy import sparse
F = np.asarray(sparse.load_npz(PATH).todense())

Dataset: Spatial Feature Tensor

Each replay contains a (T, 13, 64, 64) tensor S and a (T, 26) matrix G.

The specifics for S[t, :, :, :] is as follows:

  1. S[t, 0:8, :, :]: screen features, roughly normalized into [0-1], which is defined in Here.
  2. S[t, 8:13, :, :]: minimap features, roughly normalized into [0-1], which is defined in Here.

WARNING[Cheat Layer]: The last layer S[t, 12, :, :] refers to unit_type, which could only be obtained in replays.

Code for loading S:

import numpy as np
from scipy import sparse
S = np.asarray(sparse.load_npz(PATH).todense()).reshape([-1, 13, 64, 64])

The specifics for G[t, :] is as follows:

  1. [0-11): frame id + player info, normalized into [0, 1], which is defined Here.
  2. [11-24): cumulative score [NOT NORMALIZED], which is defined in Here.
  3. [24]: reward, i.e. final result of the game. 0: DEFEAT, 1: WIN
  4. [25]: ground truth action, ranging from [0, #ACTION].

Code for loading G:

import numpy as np
from scipy import sparse
G = np.asarray(sparse.load_npz(PATH).todense())

Build the Dataset Yourself Step by Step

Install SC2LE

  1. Download and unzip (Password: iagreetotheeula) StarCraft II Linux Packages 3.16.1 into $STAR_CRAFT$.
  2. Download and unzip (Password: iagreetotheeula) Replay Packs (3.16.1 - Pack 1, 3.16.1 - Pack 2 [Currently not used]) into $STAR_CRAFT$.

After step 1 and step 2, the folder structure is as follows:

$STAR_CRAFT$
    ├── Battle.net
    ├── Libs
    ├── Maps
    ├── Replays
    ├── SC2Data
    └── Versions
  • NOTE:
    1. $STAR_CRAFT$/Replays contains all *.SC2Replay files from 3.16.1 - Pack 1 and 3.16.1 - Pack 2 [Currently not used]
    2. $STAR_CRAFT$/Battle.net contains all contents from the folder Battle.net in 3.16.1 - Pack 1 and 3.16.1 - Pack 2 [Currently not used]

Step-by-Step Instructions

The Easy Way

The Hard Way [Step-by-Step in Details]

Requirements

future == 0.16.0

numpy == 1.13.0
scipy == 0.19.0

python_gflags == 3.1.1

tqdm == 4.14.0

protobuf == 3.4.0
pystream_protobuf == 1.4.4

PySC2 == 1.0
s2clientprotocol == 1.1