Rethinking of AlphaStar
We present a different view for AlphaStar (AS), the program achieving Grand-Master level in the game StarCraft II. It is considered big progress for AI research. However, in this paper, we present problems with the AS, some of which are the defects of it, and some of which are important details that are neglected in its article. These problems arise two questions. One is that what can we get from the built of AS? The other is that does the battle between it with humans fair? After the discussion, we present the future research directions for these problems. Our study is based on a reproduction code of the AS, and the codes are available online.
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Methods
1x1 Convolution •
Absolute Position Encodings •
Accumulating Eligibility Trace •
Adam •
Additive Attention •
AlphaStar •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
BPE •
Convolution •
Dense Connections •
Dropout •
Feedforward Network •
Global Average Pooling •
GLU •
Kaiming Initialization •
Label Smoothing •
Layer Normalization •
Linear Layer •
LSTM •
Max Pooling •
Multi-Head Attention •
Pointer Network •
Position-Wise Feed-Forward Layer •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Scaled Dot-Product Attention •
Scatter Connection •
Sigmoid Activation •
Softmax •
Tanh Activation •
TD Lambda •
Transformer •
V-trace
