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BackPolicy

Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.

Training

You can train the model by first extracting the datasets.zip file and then running the following command.

python train.py

The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.

Usage

You can retrieve the 10 most interesting moves by running.

python getMove.py

You will be asked to input a board which you can do following this syntax.

2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1

The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.

drawing

The next 4 inputs represent the white bar, black bar, white home and black home in that order. Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.

The moves are returned in the standard Backgammon notation.

1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05

Datasets

The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.

Result

The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.

Feel free to tweak the hyper parameters to improve accuracy.

Dependencies

Tensorflow > 2.9.1

About

A policy network in TensorFlow to classify backgammon moves

Topics

aibackgammonmlpolicy-networksupervised-learningtensorflow

Resources

Readme
MIT license
Activity

Stars

6 stars

Watchers

1 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
BackPolicy
Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.
Training
You can train the model by first extracting the datasets.zip file and then running the following command.
python train.py
The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.
Usage
You can retrieve the 10 most interesting moves by running.
python getMove.py
You will be asked to input a board which you can do following this syntax.
2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1
The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.
drawing
The next 4 inputs represent the white bar, black bar, white home and black home in that order.
Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.
The moves are returned in the standard Backgammon notation.
1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05
Datasets
The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.
Result
The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.
Feel free to tweak the hyper parameters to improve accuracy.
Dependencies
Tensorflow > 2.9.1
About
A policy network in TensorFlow to classify backgammon moves
Topics
aibackgammonmlpolicy-networksupervised-learningtensorflow
Resources
Readme
MIT license
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
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Repository files navigation
BackPolicy
Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.
Training
You can train the model by first extracting the datasets.zip file and then running the following command.
python train.py
The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.
Usage
You can retrieve the 10 most interesting moves by running.
python getMove.py
You will be asked to input a board which you can do following this syntax.
2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1
The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.
drawing
The next 4 inputs represent the white bar, black bar, white home and black home in that order.
Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.
The moves are returned in the standard Backgammon notation.
1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05
Datasets
The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.
Result
The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.
Feel free to tweak the hyper parameters to improve accuracy.
Dependencies
Tensorflow > 2.9.1
About
A policy network in TensorFlow to classify backgammon moves
Topics
aibackgammonmlpolicy-networksupervised-learningtensorflow
Resources
Readme
MIT license
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
BackPolicy
Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.
Training
You can train the model by first extracting the datasets.zip file and then running the following command.
python train.py
The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.
Usage
You can retrieve the 10 most interesting moves by running.
python getMove.py
You will be asked to input a board which you can do following this syntax.
2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1
The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.
drawing
The next 4 inputs represent the white bar, black bar, white home and black home in that order.
Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.
The moves are returned in the standard Backgammon notation.
1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05
Datasets
The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.
Result
The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.
Feel free to tweak the hyper parameters to improve accuracy.
Dependencies
Tensorflow > 2.9.1
About
A policy network in TensorFlow to classify backgammon moves
Topics
aibackgammonmlpolicy-networksupervised-learningtensorflow
Resources
Readme
MIT license
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
BackPolicy
Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.
Training
You can train the model by first extracting the datasets.zip file and then running the following command.
python train.py
The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.
Usage
You can retrieve the 10 most interesting moves by running.
python getMove.py
You will be asked to input a board which you can do following this syntax.
2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1
The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.
drawing
The next 4 inputs represent the white bar, black bar, white home and black home in that order.
Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.
The moves are returned in the standard Backgammon notation.
1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05
Datasets
The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.
Result
The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.
Feel free to tweak the hyper parameters to improve accuracy.
Dependencies
Tensorflow > 2.9.1
About
A policy network in TensorFlow to classify backgammon moves
Topics
aibackgammonmlpolicy-networksupervised-learningtensorflow
Resources
Readme
MIT license
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
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Repository files navigation
BackPolicy
Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.
Training
You can train the model by first extracting the datasets.zip file and then running the following command.
python train.py
The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.
Usage
You can retrieve the 10 most interesting moves by running.
python getMove.py
You will be asked to input a board which you can do following this syntax.
2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1
The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.
drawing
The next 4 inputs represent the white bar, black bar, white home and black home in that order.
Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.
The moves are returned in the standard Backgammon notation.
1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05
Datasets
The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.
Result
The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.
Feel free to tweak the hyper parameters to improve accuracy.
Dependencies
Tensorflow > 2.9.1
About
A policy network in TensorFlow to classify backgammon moves
Topics
aibackgammonmlpolicy-networksupervised-learningtensorflow
Resources
Readme
MIT license
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
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train.py
train.py
Repository files navigation
BackPolicy
Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.
Training
You can train the model by first extracting the datasets.zip file and then running the following command.
python train.py
The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.
Usage
You can retrieve the 10 most interesting moves by running.
python getMove.py
You will be asked to input a board which you can do following this syntax.
2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1
The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.
drawing
The next 4 inputs represent the white bar, black bar, white home and black home in that order.
Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.
The moves are returned in the standard Backgammon notation.
1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05
Datasets
The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.
Result
The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.
Feel free to tweak the hyper parameters to improve accuracy.
Dependencies
Tensorflow > 2.9.1
About
A policy network in TensorFlow to classify backgammon moves
Topics
aibackgammonmlpolicy-networksupervised-learningtensorflow
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Readme
MIT license
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BackPolicy
Policy network in TensorFlow to classify backgammon moves. The purpose of this network is to help a reinforcement learning model learn to play backgammon. Using a policy network allows us to greatly reduce the branching factor.
Training
You can train the model by first extracting the datasets.zip file and then running the following command.
python train.py
The network consists of 4 hidden layers with 128 neurons with an output layer consisting of 601 neurons representing each move on a backgammon board.
Usage
You can retrieve the 10 most interesting moves by running.
python getMove.py
You will be asked to input a board which you can do following this syntax.
2,0,0,0,0,-5,0,-3,0,0,0,5,-5,0,0,0,3,0,5,0,0,0,0,-2,0,0,0,0,6,3,-1
The first 24 inputs are single digit values representing each point in this order. Where black checkers are negative values and white are positive.
drawing
The next 4 inputs represent the white bar, black bar, white home and black home in that order.
Then comes 2 inputs being each dice. Lastly we have a value being either 1 or -1 where 1 represent white moving and black being black moving.
The moves are returned in the standard Backgammon notation.
1: 24/18 : 0.9781245
------------------------
2: 8/2 : 0.008682405
------------------------
3: Cannot/move : 0.007958977
------------------------
4: 24/15 : 0.0024637433
------------------------
5: 6/3 : 0.00073977234
------------------------
6: 8/5 : 0.00060132524
------------------------
7: 24/21 : 0.0004732638
------------------------
8: 8/3 : 0.00044788906
------------------------
9: 13/7 : 0.00035200707
------------------------
10: 13/5 : 8.882125e-05
Datasets
The datasets contain over 2 million different board positions with the corresponding correct move. All data points were scraped from games between high-level players.
Result
The network achieves a top 10 accuracy of 99% whilst having a regular accuracy of 60%. The top 10 accuracy is the more important metric in this scenario as we will branch over at least 10 moves in the reinforcement learning model.
Feel free to tweak the hyper parameters to improve accuracy.
Dependencies
Tensorflow > 2.9.1
About
A policy network in TensorFlow to classify backgammon moves
Topics
aibackgammonmlpolicy-networksupervised-learningtensorflow
Resources
Readme
MIT license
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages