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Deep Reinforcement Learning Nanodegree - Project 1: Navigation

Introduction

For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.

Without trainningTrainned

Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.

The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:

  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.

To complete the task the agent must get an average score of 13 over 100 consecutive episodes.

Starting

  1. Clone this repo.

  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning

  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.

  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.

Instructions

Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.

  1. For training from zero run all the cells inside the navigation notebook

  2. For testing skip the training section and follow the instructions to load the weights.

Download the environment (not Linux users)

You need to select the environment that matches your operating system:

  • Linux: click here

  • Mac OSX: click here

  • Windows (32-bit): click here

  • Windows (64-bit): click here

About

Project 1 of Udacity's Deep Reinforcement Learning Nanodegree

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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Deep Reinforcement Learning Nanodegree - Project 1: Navigation
Introduction
For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.
Without trainningTrainned
Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:
  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.
To complete the task the agent must get an average score of 13 over 100 consecutive episodes.
Starting
  1. Clone this repo.
  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning
  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.
  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.
Instructions
Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.
  1. For training from zero run all the cells inside the navigation notebook
  2. For testing skip the training section and follow the instructions to load the weights.
Download the environment (not Linux users)
You need to select the environment that matches your operating system:
  • Linux: click here
  • Mac OSX: click here
  • Windows (32-bit): click here
  • Windows (64-bit): click here
About
Project 1 of Udacity's Deep Reinforcement Learning Nanodegree
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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Packages
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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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Deep Reinforcement Learning Nanodegree - Project 1: Navigation
Introduction
For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.
Without trainningTrainned
Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:
  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.
To complete the task the agent must get an average score of 13 over 100 consecutive episodes.
Starting
  1. Clone this repo.
  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning
  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.
  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.
Instructions
Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.
  1. For training from zero run all the cells inside the navigation notebook
  2. For testing skip the training section and follow the instructions to load the weights.
Download the environment (not Linux users)
You need to select the environment that matches your operating system:
  • Linux: click here
  • Mac OSX: click here
  • Windows (32-bit): click here
  • Windows (64-bit): click here
About
Project 1 of Udacity's Deep Reinforcement Learning Nanodegree
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Deep Reinforcement Learning Nanodegree - Project 1: Navigation
Introduction
For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.
Without trainningTrainned
Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:
  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.
To complete the task the agent must get an average score of 13 over 100 consecutive episodes.
Starting
  1. Clone this repo.
  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning
  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.
  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.
Instructions
Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.
  1. For training from zero run all the cells inside the navigation notebook
  2. For testing skip the training section and follow the instructions to load the weights.
Download the environment (not Linux users)
You need to select the environment that matches your operating system:
  • Linux: click here
  • Mac OSX: click here
  • Windows (32-bit): click here
  • Windows (64-bit): click here
About
Project 1 of Udacity's Deep Reinforcement Learning Nanodegree
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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" + '

Skip to content
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Deep Reinforcement Learning Nanodegree - Project 1: Navigation
Introduction
For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.
Without trainningTrainned
Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:
  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.
To complete the task the agent must get an average score of 13 over 100 consecutive episodes.
Starting
  1. Clone this repo.
  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning
  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.
  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.
Instructions
Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.
  1. For training from zero run all the cells inside the navigation notebook
  2. For testing skip the training section and follow the instructions to load the weights.
Download the environment (not Linux users)
You need to select the environment that matches your operating system:
  • Linux: click here
  • Mac OSX: click here
  • Windows (32-bit): click here
  • Windows (64-bit): click here
About
Project 1 of Udacity's Deep Reinforcement Learning Nanodegree
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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 = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Deep Reinforcement Learning Nanodegree - Project 1: Navigation
Introduction
For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.
Without trainningTrainned
Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:
  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.
To complete the task the agent must get an average score of 13 over 100 consecutive episodes.
Starting
  1. Clone this repo.
  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning
  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.
  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.
Instructions
Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.
  1. For training from zero run all the cells inside the navigation notebook
  2. For testing skip the training section and follow the instructions to load the weights.
Download the environment (not Linux users)
You need to select the environment that matches your operating system:
  • Linux: click here
  • Mac OSX: click here
  • Windows (32-bit): click here
  • Windows (64-bit): click here
About
Project 1 of Udacity's Deep Reinforcement Learning Nanodegree
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Deep Reinforcement Learning Nanodegree - Project 1: Navigation
Introduction
For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.
Without trainningTrainned
Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:
  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.
To complete the task the agent must get an average score of 13 over 100 consecutive episodes.
Starting
  1. Clone this repo.
  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning
  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.
  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.
Instructions
Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.
  1. For training from zero run all the cells inside the navigation notebook
  2. For testing skip the training section and follow the instructions to load the weights.
Download the environment (not Linux users)
You need to select the environment that matches your operating system:
  • Linux: click here
  • Mac OSX: click here
  • Windows (32-bit): click here
  • Windows (64-bit): click here
About
Project 1 of Udacity's Deep Reinforcement Learning Nanodegree
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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Deep Reinforcement Learning Nanodegree - Project 1: Navigation
Introduction
For this project, I’ve trainned an agent to navigate and collect bananas in a square world using Unit environment. You can find bellow the two conditions: without any trainning and trainned agent.
Without trainningTrainned
Each time the agent collect a yellow banana, it’s given a reward of +1. For each blue banana, it received a -1 reward. The goal of the agent is to collect as many yellow bananas as possible and avoid any blue bananas, as it must increase the score given by the amount of rewards received.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. 4 discrete actions are available, corresponding to:
  • 0 - move forward.
  • 1 - move backward.
  • 2 - turn left.
  • 3 - turn right.
To complete the task the agent must get an average score of 13 over 100 consecutive episodes.
Starting
  1. Clone this repo.
  2. Setup the python enviroment following next link: udacity/deep-reinforcement-learning
  3. Copy the content of the p1_navigation/ folder from this repo to the p1_navigation/ folder of the udacity/deep-reinforcement-learning repo and replaces or remove existing files.
  4. Unzip the Banana_Linux.zip file that is located under the p1_navigation/ folder under the same directory. If you are not using Linux, follow the instructions on the botton of this file.
Instructions
Open a jupyter notebook and open the Navigation.ipynb to train or test the agent.
  1. For training from zero run all the cells inside the navigation notebook
  2. For testing skip the training section and follow the instructions to load the weights.
Download the environment (not Linux users)
You need to select the environment that matches your operating system:
  • Linux: click here
  • Mac OSX: click here
  • Windows (32-bit): click here
  • Windows (64-bit): click here
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Project 1 of Udacity's Deep Reinforcement Learning Nanodegree
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