Skip to content

Latest commit

 

History

History
28 lines (17 loc) · 1.27 KB

README.md

File metadata and controls

28 lines (17 loc) · 1.27 KB
Raw

Reinforcement Learning

Requirements

  • Be sure that you had built the engine. The engine should be in the build directory. Recommend to use the -DUSE_ZLIB=1 option.
  • PyTorch 1.x (for python)
  • NumPy (for python)

Note

Use the default setting in the bash directory. The network will reach strong amateur level in 1 ~ 2 weeks on 19x19 with the RTX 2070s computer.

Simple Usage

There are two bash files. The setup.sh will do the initialization. Copy the training script and engine to this directory. The selfplay.sh will do the self-play and trainig loop.

$ cp -r bash selfplay-course
$ cd selfplay-course
$ bash setup.sh -s ..
$ bash selfplay.sh

The selfplay.sh will do the infinite loop. If you want to stop the loop, you need to create a kill file and wait for end of this round.

$ touch kill.txt

Sample Configuration File

The sample directory includes some enigne self-play Configuration files. The sample/full-gumbel-p16.txt will do full Gumbel learning with 16 playouts. The sample/full-alphazero-p400.txt will do full AlphaZero learning with 400 playouts. You may simply reuse the files for customization learning. And please see this section. It explains the configuration and training parameters.