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

• Global features are available HERE.
• [TRAIN|VAL|TEST] split is available HERE.
• [Stat] is available HERE. The stat files with postfix _human.json are human-readable.
• Spatial features are NOT avaiable since I do not have any download server. Please follow the instructions to generate the spatial features by yourself.

Baselines

Global State Evaluation

Build Order Prediction

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