wangjia184 / renju

A Reinforcement-Learning based Renju game

A Reinforcement-Learning based Renju game

This is my personal project to practice reinforcement learning. You can download the application and play with it.

Renju is a professional variant of gomoku(five in a row) by adding following restrictions to black stones to weaken the advantages of the first player in the game.

• Double three – Black cannot place a stone that builds two separate lines with three black stones in unbroken rows (i.e. rows not blocked by white stones).
• Double four – Black cannot place a stone that builds two separate lines with four black stones in a row.
• Overline – six or more black stones in a row.

Overview

• AI is implemented using residual Convolutional Neural Network and Monte Carlo Tree Search.
• AI is not told how to play game. AI learns how to play the game by playing with itself (a.k.a Self Player).
• Algorithm design is modified from Alpha Go Zero
  • AlphaGo: How it works technically?
  • AlphaZero - A step-by-step look at Alpha Zero and Monte Carlo Tree Search
  • AlphaGo Zero — a game changer. (How it works?)
  • Lessons From Alpha Zero 1 2 3 4 5 6
• The application is developed in Rust language to avoid performance bottleneck in Python for MCTS.
• Self-play is much slower than training. Hence Self-Play is opitimized to use quantization. It shows better performance than GPU on Mac M1.
• A novel lock-free tree implementation for MCTS.

Neural Network

The following graph shows the policy-value network from AlphaGo Zero.

It has been simplified here:

1. Number of residual blocks is reduced from 39 to 19.
2. Residual block width is narrowed from 256 filters down to 64 filters.
3. Since width is reduced to 1/4 and dying ReLU problem was encountered in first attempt, hence activation function ReLU of last dense layer is replaced with ELU.
4. Input is simplified to a (1, 4, 15, 15) NCHW tensor with four 15x15 planes.
   • The first plane represents stones of current player
   • The second plane represents stones of opponent player
   • The third plane represents the position of last move
   • The fourth plane are filled with ones if current player is black; or zeros if white.
5. Loss function

$$ l = (z-v)^{2}-\pi ^{T}ln(p)+c\left || \theta \right ||^{2} $$