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AlphaFy

AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network). The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.

Requirement

  1. Python 3.6
  2. Keras

Usage

A tutorial is available in the src folder.

  1. Create a player.

    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()

  2. Collect data via self-playing.

    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)

  3. Save and load.

    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)

  4. Train the model.

    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)

  5. Evaluate performance.

    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)

Example

This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512), guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.

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+-------------------+
1 2 3 4 5 6 7 8 9

About

A Gomoku AI based on AlphaGo Zero's Algorithm.

Topics

aialphagogomokumcts

Resources

Readme
MIT license
Activity

Stars

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Watchers

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Report repository

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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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AlphaFy
AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network).
The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.
Requirement
  1. Python 3.6
  2. Keras
Usage
A tutorial is available in the src folder.
  1. Create a player.
    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()
  2. Collect data via self-playing.
    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)
  3. Save and load.
    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)
  4. Train the model.
    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)
  5. Evaluate performance.
    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)
Example
This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512),
guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.
 1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . . . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
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+-------------------+
1 2 3 4 5 6 7 8 9

About
A Gomoku AI based on AlphaGo Zero's Algorithm.
Topics
aialphagogomokumcts
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
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var style = document.createElement('style');
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.markdown-body {
color-scheme: dark light;
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var __m = "*";
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Skip to content
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23 Commits
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Repository files navigation
AlphaFy
AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network).
The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.
Requirement
  1. Python 3.6
  2. Keras
Usage
A tutorial is available in the src folder.
  1. Create a player.
    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()
  2. Collect data via self-playing.
    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)
  3. Save and load.
    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)
  4. Train the model.
    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)
  5. Evaluate performance.
    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)
Example
This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512),
guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.
 1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . . . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . O). . . |
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1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9

About
A Gomoku AI based on AlphaGo Zero's Algorithm.
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AlphaFy
AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network).
The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.
Requirement
  1. Python 3.6
  2. Keras
Usage
A tutorial is available in the src folder.
  1. Create a player.
    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()
  2. Collect data via self-playing.
    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)
  3. Save and load.
    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)
  4. Train the model.
    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)
  5. Evaluate performance.
    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)
Example
This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512),
guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.
 1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . . . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
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3 | . . . . . . . . . |
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1 | . . . . . . . . . |
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1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
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+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . X . . . |
6 | . . . . . . . . . |
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+-------------------+
9 | . . . . . . . . . |
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6 | . . . . . . . . . |
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1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O)X . . . |
6 | . . . . . . . . . |
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+-------------------+
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+-------------------+
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+-------------------+
9 | . . . . . . . . . |
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+-------------------+
9 | . . . . . . . . . |
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+-------------------+
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+-------------------+
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+-------------------+
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2 | . . . . . . . . . |
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+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
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1 2 3 4 5 6 7 8 9
+-------------------+
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2 | . . . . . . . . . |
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+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
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7 | . . . X O X . . . |
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5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
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+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . X O O O O O). |
5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9

About
A Gomoku AI based on AlphaGo Zero's Algorithm.
Topics
aialphagogomokumcts
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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Repository files navigation
AlphaFy
AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network).
The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.
Requirement
  1. Python 3.6
  2. Keras
Usage
A tutorial is available in the src folder.
  1. Create a player.
    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()
  2. Collect data via self-playing.
    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)
  3. Save and load.
    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)
  4. Train the model.
    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)
  5. Evaluate performance.
    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)
Example
This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512),
guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.
 1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . . . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . O). . . |
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3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
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1 2 3 4 5 6 7 8 9
+-------------------+
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3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X). X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O)X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . . . . . |
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1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
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+-------------------+
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1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
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2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
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+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
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+-------------------+
9 | . . . . . . . . . |
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+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
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2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
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6 | . . . . O O O . . |
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3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . O)O O O . . |
5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . X)O O O O . . |
5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . X O O O O O). |
5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9

About
A Gomoku AI based on AlphaGo Zero's Algorithm.
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var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
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lastUrl = location.href;
setTimeout(tryTheater, 500);
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observer.observe(document.body, { childList: true, subtree: true });
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AlphaFy
AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network).
The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.
Requirement
  1. Python 3.6
  2. Keras
Usage
A tutorial is available in the src folder.
  1. Create a player.
    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()
  2. Collect data via self-playing.
    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)
  3. Save and load.
    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)
  4. Train the model.
    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)
  5. Evaluate performance.
    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)
Example
This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512),
guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.
 1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . . . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . O). . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . X). . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O). . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X). X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O)X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | X). . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . . O). . |
5 | . . . . . O . . . |
4 | X . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . . O . . |
5 | . . . . . O . . . |
4 | X . . . O . . . . |
3 | . . . X). . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . O)O . . |
5 | . . . . . O . . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . O O . . |
5 | . . . . . O X). . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . O)O O . . |
5 | . . . . . O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . O O O . . |
5 | . . . . X)O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . O)O O O . . |
5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . X)O O O O . . |
5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . X O O O O O). |
5 | . . . . X O X . . |
4 | X . . . O . . . . |
3 | . . . X . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9

About
A Gomoku AI based on AlphaGo Zero's Algorithm.
Topics
aialphagogomokumcts
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
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})();
(function(){
try {
var __m = "*";
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Skip to content
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Repository files navigation
AlphaFy
AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network).
The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.
Requirement
  1. Python 3.6
  2. Keras
Usage
A tutorial is available in the src folder.
  1. Create a player.
    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()
  2. Collect data via self-playing.
    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)
  3. Save and load.
    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)
  4. Train the model.
    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)
  5. Evaluate performance.
    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)
Example
This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512),
guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.
 1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . . . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . . . . . |
6 | . . . . . . . . . |
5 | . . . . . O). . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . X). . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . . . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . . . X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O). . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X). X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O)X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | . . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . . . . . |
5 | . . . . . O . . . |
4 | X). . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . . O). . |
5 | . . . . . O . . . |
4 | X . . . O . . . . |
3 | . . . . . . . . . |
2 | . . . . . . . . . |
1 | . . . . . . . . . |
+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
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2 | . . . . . . . . . |
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+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
9 | . . . . . . . . . |
8 | . . . . . . . . . |
7 | . . . X O X . . . |
6 | . . . . . O)O . . |
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2 | . . . . . . . . . |
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+-------------------+
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
+-------------------+
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+-------------------+
1 2 3 4 5 6 7 8 9

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AlphaFy
AlphaFy is a Gomoku AI based on AlphaGo Zero's Algorithm. It use the same structure as AlphaGo Zero (Monte Carlo Tree Search and Residual Network).
The game's board size is scalable, so you can try your algorithm on 3x3 Tic-tac-toe and then scale it to a bigger board like a 9x9 gomoku.
Requirement
  1. Python 3.6
  2. Keras
Usage
A tutorial is available in the src folder.
  1. Create a player.
    fromplayerimportPlayer# This will give you a untrained random model.# For a pretrained model, use:# fy = Player('latest')fy=Player()
  2. Collect data via self-playing.
    # This option turn on a rule-based AI as a guide, but it's very slow.# Turn it off when the AI seems to understand its goal.fy.enable_guide()
# This indicate how many nodes in Monte Carlo Tree will be expanded to pick each move. fy.set_thinking_depth(128)
# Data will be collected after each game ended.fy.self_play(show_board=True)
  3. Save and load.
    # Save model and data separately.# It's equal to:# fy.save('example', override=True)fy.save_data('example', override=True)
fy.save_model('example', override=True)
fy.load_model('example')
# If merge=False, it will override current data.fy.load_data('example', merge=False)
  4. Train the model.
    # Note that it needs a lot of data to obtain a reasonable performance.fy.train(epochs=5, batch_size=128)
  5. Evaluate performance.
    fy.vs_user(first_hand=True)
# Or you can compare between two models.rival=Player('latest')
fy.vs(rival)
Example
This is an example game record of the pretrain model's self-play, under the config of 512 nodes expaned each move(thinking-depth=512),
guide turned off(fy.disable_guide()), 8 threads(changeable in config.py). To achieve a human-level performance, set thinking-depth to 30000 or higher.
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1 2 3 4 5 6 7 8 9

About
A Gomoku AI based on AlphaGo Zero's Algorithm.
Topics
aialphagogomokumcts
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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Packages
Contributors
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