AI learns to play snake using Genetic Algorithm and Deep learning

Описание

Using a neural network and the genetic algorithm I trained an AI to play snake.

Time Passing By by Audionautix is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/)
Artist: http://audionautix.com/

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AI learns to play the WORLDS HARDEST GAME even more levels

AI learns to play the WORLDS HARDEST GAME even more levels

Using the genetic algorithm I trained an Ai to play even more levels of the worlds hardest game. check out previous videos https://www.youtube.com/watch?v=kVwkLb8zxq0&t=353s https://www.youtube.com/watch?v=Yo2SepcNyw4&t=3s I will upload the code soon so you can run these things yourself. Twitter: https://twitter.com/code_bullet Patreon: https://www.patreon.com/CodeBullet Discord: https://discord.gg/UZDMYx5

6 месяцев назад
Google Deep Mind AI Alpha Zero Refutes 1.e4

Google Deep Mind AI Alpha Zero Refutes 1.e4

#agadmator Check out all my videos on this match https://www.youtube.com/playlist?list=PLDnx7w_xuguHIxbL7akaYgEvV4spwYkmn Read more about Deep Mind Alpha Zero here https://arxiv.org/pdf/1712.01815.pdf Link to the other games https://lichess.org/study/wxrovYNH A chess game between Deep Mind Alpha Zero and Stockfish Google Deep Mind Alpha Zero vs Stockfish One of the games 1. d4 e6 2. Nc3 Nf6 3. e4 d5 4. e5 Nfd7 5. f4 c5 6. Nf3 Nc6 7. Be3 Be7 8. Qd2 a6 9. Bd3 c4 10. Be2 b5 11. a3 Rb8 12. O-O O-O 13. f5 a5 14. fxe6 fxe6 15. Bd1 b4 16. axb4 axb4 17. Ne2 c3 18. bxc3 Nb6 19. Qe1 Nc4 20. Bc1 bxc3 21. Qxc3 Qb6 22. Kh1 Nb2 23. Nf4 Nxd1 24. Rxd1 Bd7 25. h4 Ra8 26. Bd2 Rfb8 27. h5 Rxa1 28. Rxa1 Qb2 29. Qxb2 Rxb2 30. c3 Rb3 31. Ra8+ Rb8 32. Ra2 Rb3 33. g4 Ra3 34. Rb2 Kf7 35. Kg2 Bc8 36. Rb6 Ra6 37. Rb1 Ke8 38. Kg3 h6 39. Ng6 Ra3 40. Rb6 Bd7 41. g5 hxg5 42. Kg4 Bd8 43. Rb2 Bc8 44. Nxg5 Ra1 45. Nf3 Ra3 46. Be1 Ba5 47. Rf2 Ra1 48. Bd2 Bd8 49. Rh2 Ne7 50. Bg5 Nf5 51. Bxd8 Kxd8 52. Rb2 Rc1 53. Ngh4 Nxh4 54. Nxh4 Bd7 55. Rb8+ Bc8 56. Ng2 Rxc3 57. Nf4 Rc1 58. Ra8 Kd7 59. Kf3 Rc3+ 60. Kf2 Ke7 61. Kg2 Kf7 62. Ng6 Ke8 63. Ra1 Rc7 64. Kh3 Rf7 65. Kg4 Kd8 66. Nf4 Bd7 67. Ra7 Kc8 68. Kg3 Re7 69. Nd3 Kb8 70. Ra6 Bc8 71. Rb6+ Kc7 72. Rd6 Kb8 73. Nc5 g6 74. h6 Rh7 75. Nxe6 Rxh6 76. Nf4 Rh1 77. Nxd5 Rh3+ 78. Kf4 Rh4+ 79. Ke3 Rh3+ 80. Kd2 Bf5 81. Ne7 Rh2+ 82. Ke3 Bh3 83. Nxg6 Rh1 84. Nf4 Bg4 85. Rf6 Kc7 86. Nd3 Bd7 87. d5 Bb5 88. Nf4 Ba4 89. Kd4 Be8 90. Rf8 Rd1+ 91. Kc5 Rc1+ 92. Kb4 Rb1+ 93. Kc3 Bb5 94. Kd4 Ba6 95. Rf7+ ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- If you realllly enjoy my content, you're welcome to support me and my channel with a small donation via PayPal, Bitcoin or Litecoin. Link to PayPal donation https://www.paypal.me/agadmator Bitcoin address 12VEbMQPyLzBoZzw9yuNofph4C9Ansc4iZ Litecoin address LbSuZuBffDCNmr5CSZbY7W2zM83w4ZvnC7 Check out ALL my videos here https://www.youtube.com/watch?v=f-ZOwHdNLO0&list=PLDnx7w_xuguFTxcfiM11bB1JchtHclEJg Facebook: https://www.facebook.com/agadmatoryoutube Twitch: https://www.twitch.tv/agadmatorchess Twitter: https://twitter.com/agadmator Instagram: https://www.instagram.com/agadmator/ Lichess: https://lichess.org/@/agadmator Chess.com: agadmator Skype: agadmator League of Legends: agadmator :)

1 лет назад
Hello World - Machine Learning Recipes #1

Hello World - Machine Learning Recipes #1

Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up. Follow https://twitter.com/random_forests for updates on new episodes! Subscribe to the Google Developers: http://goo.gl/mQyv5L - Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG And here's our playlist: https://goo.gl/KewA03

3 лет назад
What is backpropagation really doing? | Deep learning, chapter 3

What is backpropagation really doing? | Deep learning, chapter 3

What's actually happening to a neural network as it learns? Next video: https://youtu.be/tIeHLnjs5U8 Training data generation: http://3b1b.co/crowdflower Find the full playlist at http://3b1b.co/neural-networks The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen's book or Chis Olah's blog. Thanks to everyone supporting on Patreon. http://3b1b.co/nn3-thanks http://3b1b.co/support For more on backpropagation: http://neuralnetworksanddeeplearning.com/chap2.html https://github.com/mnielsen/neural-networks-and-deep-learning http://colah.github.io/posts/2015-08-Backprop/ Music by Vincent Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown

1 лет назад
The Perfect Snake Game World Record Highscore

The Perfect Snake Game World Record Highscore

Snake Game is the common name for a videogame concept where the player maneuvers a line which grows in length, with the line itself being a primary obstacle. ► Full serie of People Doing Awesome Things here: https://goo.gl/tP4Y1c ► Girls Like a Boss Compilation here: https://youtu.be/NTaF_mQRh2g ► If you want, follow us on instagram: https://www.instagram.com/wanapedia/ ► Want to see more videos? Join the channel and help us reach 100,000 subscribers: https://goo.gl/ohH20n ► If you liked the music, you can get it in the links below: Track: LFZ - Echos (Meikal Remix) Music provided by NoCopyrightSounds. Watch: https://youtu.be/nMldNLiYzGU

1 лет назад
Sorting Algorithms (Bubble Sort, Shell Sort, Quicksort)

Sorting Algorithms (Bubble Sort, Shell Sort, Quicksort)

A visualization of the following sorting algorithms: * Bubble Sort * Shell Sort (Donald Shell, 1959) * Quicksort (Tony Hoare, 1960) Music by Chris Zabriskie (http://www.youtube.com/chriszabriskie)

4 лет назад
5 Must Have Skills To Become Machine Learning Engineer

5 Must Have Skills To Become Machine Learning Engineer

Hello Everyone!!! Let's check out what are the 5 must-have skills to become a machine learning engineer. First, let's understand what machine learning is. In simple words., Machine learning is all about making the computers to perform intelligent tasks without explicitly coding. This is achieved by training the computer with lots and lots of data. For example: Detecting whether a mail is a spam or not, recognizing handwritten digits, Fraud detection in Transactions... and many such applications... Now let's see what are the top 5 skills to get a machine learning job. 1). At number 1, we have Math Skills: Under math skills, we need to know probability and statistics, linear algebra and calculus. Probability and Statistics: Machine learning is very much closely related to statistics. You need to know the fundamentals of statistics and probability theory, descriptive statistics, Baye's rule and random variables, probability distributions, sampling, hypothesis testing, regression and decision analysis. Linear Algebra: You need to know how to with matrices and some basic operations on matrices such as matrix addition, subtraction, scalar and vector multiplication, inverse, transpose and vector spaces. Calculus: In calculus, you need to know the basics of differential and integral calculus. 2). At number two we have Programming skills: A little bit of coding skills is enough. But it's preferred to have the knowledge of data structures, algorithms and Object Oriented Programming (or OOPs) concepts. Some of the popular programming languages to learn for machine learning is Python, R, Java, and C++. It's your preference to master any one programming language. But its advisable to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one. 3). At number 3 we have Data engineer skills: Ability to work with large amounts of data (or big data), Data preprocessing, the knowledge of SQL and NoSQL, ETL (or Extract Transform and Load) operations, data analysis and visualization skills. 4). Next, we have Knowledge of Machine Learning Algorithms: you should be familiar with popular machine learning algorithms such as linear regression, logistic regression, decision trees, random forest, clustering (like K means, hierarchical), reinforcement learning and neural networks. 5). And Finally, The Knowledge of Machine Learning Frameworks: You Should be Familiar with popular machine learning frameworks such as sci-kit learn, tensorflow, Azure, caffe, theano, spark and torch. Music: www.bensound.com

1 лет назад
Pathfinding Algorithms

Pathfinding Algorithms

http://dperrysvendsen.wordpress.com/2014/12/05/pathfinding-algorithms/ This program was originally built to demonstrate the relative efficiency of different pathfinding algorithms in finding the shortest path between two points on a map. Three algorithms are built in: • Breadth-first search, an algorithm traditionally used to navigate small, enclosed areas. • Best-first search, an algorithm generally better suited to more open environments with fewer obstacles. • A* search, a somewhat more complex algorithm designed to intelligently dodge obstacles. To represent the map, the program uses a grid of nodes, in which each node has up to four traversable edges: up, down, left and right. One node is designated the root node, and another the target node. In addition, a node can be marked as impassable, effectively creating an obstacle around which an algorithm must navigate. In order to generate a path, each algorithm utilises an open set, a collection of nodes representing the boundary of an increasing search area. The algorithm gradually expands the search area by evaluating one node at a time from its open set. Evaluating a node involves first checking if it is the target node – if this is the case, a path has been found and the algorithm terminates. Failing this, the node is removed from the open set and marked as visited so that is will not be re-added (this prevents the algorithm from generating loops). Finally, each of the nodes immediate unvisited neighbours are added to the open set. Crucially, for each of these neighbouring nodes, the current node is marked as their predecessor. This search area continues to expand until either it reaches the target node (meaning a path was been found), or there are no new nodes to evaluate (meaning no path was found). If a path is found, it is then reconstructed based on the predecessor of each node, starting from the target node, and continuing until the root node is reached. The difference between each algorithm lies in how they decide the order in which the Nodes in the open set are evaluated. • Breadth-first search uses a Queue, which functions much like a real-world queue in ensuring that Nodes are evaluated in the same order they were added. • Best-first search uses a List, assigning each Node a heuristic value based on its estimated distance from the target node, not taking into account any obstacles. This value is simply the rectilinear distance, or the sum of the horizontal and vertical offsets, between the two points. The Node with the lowest heuristic value is then chosen to be evaluated. • A* search also uses a List, and also assigns each Node a heuristic value. However, it adds this heuristic value to the cumulative cost (the path length) to generate the Node’s f-score. The Node with the lowest f-score is then chosen to be evaluated.

4 лет назад
How the Universe Works -   The Great Secret of Black Holes - Space Discovery Documentary

How the Universe Works - The Great Secret of Black Holes - Space Discovery Documentary

A black hole is a mathematically defined region of spacetime exhibiting such a strong gravitational pull that no particle or electromagnetic radiation can escape from it. The theory of general relativity predicts that a sufficiently compact mass can deform spacetime to form a black hole. The boundary of the region from which no escape is possible is called the event horizon. Although crossing the event horizon has enormous effect on the fate of the object crossing it, it appears to have no locally detectable features. In many ways a black hole acts like an ideal black body, as it reflects no light. Moreover, quantum field theory in curved spacetime predicts that event horizons emit Hawking radiation, with the same spectrum as a black body of a temperature inversely proportional to its mass. This temperature is on the order of billionths of a kelvin for black holes of stellar mass, making it essentially impossible to observe. Objects whose gravitational fields are too strong for light to escape were first considered in the 18th century by John Michell and Pierre-Simon Laplace. The first modern solution of general relativity that would characterize a black hole was found by Karl Schwarzschild in 1916, although its interpretation as a region of space from which nothing can escape was first published by David Finkelstein in 1958. Long considered a mathematical curiosity, it was during the 1960s that theoretical work showed black holes were a generic prediction of general relativity. The discovery of neutron stars sparked interest in gravitationally collapsed compact objects as a possible astrophysical reality. Black holes of stellar mass are expected to form when very massive stars collapse at the end of their life cycle. After a black hole has formed, it can continue to grow by absorbing mass from its surroundings. By absorbing other stars and merging with other black holes, supermassive black holes of millions of solar masses (M☉) may form. There is general consensus that supermassive black holes exist in the centers of most galaxies. Despite its invisible interior, the presence of a black hole can be inferred through its interaction with other matter and with electromagnetic radiation such as visible light. Matter falling onto a black hole can form an accretion disk heated by friction, forming some of the brightest objects in the universe. If there are other stars orbiting a black hole, their orbit can be used to determine its mass and location. Such observations can be used to exclude possible alternatives (such as neutron stars). In this way, astronomers have identified numerous stellar black hole candidates in binary systems, and established that the radio source known as Sgr A*, at the core of our own Milky Way galaxy, contains a supermassive black hole of about 4.3 million M☉. #UniverseDocumentary #Universe #SpaceDocumentary

4 недель назад
Deep Learning Cars

Deep Learning Cars

A small 2D simulation in which cars learn to maneuver through a course by themselves, using a neural network and evolutionary algorithms. Interested in how Neural Networks work? Have a look at my one-minute-explanation: https://www.youtube.com/watch?v=rEDzUT3ymw4 This simulation was implemented in Unity. You can find detailed information about how this simulation works, as well as a link to the entire source code on my website: https://arztsamuel.github.io/en/projects/unity/deepCars/deepCars.html Don't miss any future videos, by subscribing to my channel. Follow me on Twitter: https://twitter.com/SamuelArzt

2 лет назад