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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Google Deep Mind AI Alpha Zero Refutes 1.e4

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#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 :)

9 месяцев назад
How Machines Learn

How Machines Learn

How do all the algorithms around us learn to do their jobs? Bot Wallpapers on Patreon: https://www.patreon.com/posts/15959388 Discuss this video: https://www.reddit.com/r/CGPGrey/comments/7klmd3/how_do_machines_learn/ Footnote: https://www.youtube.com/watch?v=wvWpdrfoEv0 Podcasts: https://www.youtube.com/user/HelloInternetPodcast https://www.youtube.com/channel/UCqoy014xOu7ICwgLWHd9BzQ Thank you to my supporters on Patreon: James Bissonette, James Gill, Cas Eliëns, Jeremy Banks, Thomas J Miller Jr MD, Jaclyn Cauley, David F Watson, Jay Edwards, Tianyu Ge, Michael Cao, Caron Hideg, Andrea Di Biagio, Andrey Chursin, Christopher Anthony, Richard Comish, Stephen W. Carson, JoJo Chehebar, Mark Govea, John Buchan, Donal Botkin, Bob Kunz https://www.patreon.com/cgpgrey How neural networks really work with the real linear algebra: https://www.youtube.com/watch?v=aircAruvnKk Music by: http://www.davidreesmusic.com

9 месяцев назад
Agar.io - a fascinating bot

Agar.io - a fascinating bot

An agar.io bot at its best. It usually does not work that well. If you are interested in downloading the Bot: https://github.com/Apostolique/Agar.io-bot This was recorded live at http://www.twitch.tv/brunnernathan Here are some lazzy tags: agar.io, agario, bot, hack, gameplay

3 лет назад
7 Seemingly Impossible Levels in Super Mario Maker.

7 Seemingly Impossible Levels in Super Mario Maker.

Most of the times when I run into a seemingly impossible level in Super Mario Maker, then solution is easy: Search the Super Mario Maker level for a hidden question block, and use the dev exit. There are way too many hidden question block exits in Super Mario Maker, since there are so many cool and much better ways to create a seemingly impossible level in Super Mario Maker. Today we are going to take a look at 7 seemingly impossible rooms in Super Mario Maker and how to actually escape them! --------------------------------------------------------- Link to Tatiaus most recent Super Mario Maker glitch and tricks showcase: https://www.youtube.com/watch?v=ffh4dtyqOGI&t= Link to the Super Mario Maker Kaizo Wiki: http://kaizomariomaker.wikia.com/wiki/Kaizo_Mario_Maker_Wikia -------------Credits for the Music--------------- ------Holfix https://www.youtube.com/holfix HolFix - Beyond The Kingdom https://www.youtube.com/watch?v=2CiGpsBLBX8 ------Kevin MacLeod "Adventure Meme", “Amazing Plan”, “The Builder” Kevin MacLeod incompetech.com Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/

4 недель назад
Mathematics of Machine Learning

Mathematics of Machine Learning

Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I'm going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we'll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field. Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693

6 месяцев назад
MarI/O - Machine Learning for Video Games

MarI/O - Machine Learning for Video Games

MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World. Source Code: http://pastebin.com/ZZmSNaHX "NEAT" Paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Some relevant Wikipedia links: https://en.wikipedia.org/wiki/Neuroevolution https://en.wikipedia.org/wiki/Evolutionary_algorithm https://en.wikipedia.org/wiki/Artificial_neural_network BizHawk Emulator: http://tasvideos.org/BizHawk.html SethBling Twitter: http://twitter.com/sethbling SethBling Twitch: http://twitch.tv/sethbling SethBling Facebook: http://facebook.com/sethbling SethBling Website: http://sethbling.com SethBling Shirts: http://sethbling.spreadshirt.com Suggest Ideas: http://reddit.com/r/SethBlingSuggestions Music at the end is Cipher by Kevin MacLeod

3 лет назад
Evolving Neural Networks to Play 2048

Evolving Neural Networks to Play 2048

John Downey's final project for Modern Robots: Evolutionary Robotics.

4 лет назад
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

4 недель назад
Evolution of Snake Games 1976-2018

Evolution of Snake Games 1976-2018

Evolution of Snake Games 1976-2018 Barricade 1976 Bigfoot Bonkers 1976 Blockade 1976 Dominos 1977 Surround 1977 Snafu 1981 Nibbler 1982 Snake Byte 1982 Tron 1982 Knot in 3D 1983 Serpent 1990 Nibbles 1991 Rattler Race 1991 BeamWars 1992 Achtung, die Kurve! 1995 Snake 1997 GLtron 1998 Snake 2000 Armagetron Advanced 2001 Warring Worms 2002 Snake EX2 2003 Snakes 2005 Snakes 3D 2005 Snake III 2005 Snake Xenzia 2006 Snakeball 2007 Nokia Subsonic 2008 LightBike 2009 Nimble Quest 2013 Pix the Cat 2014 Snake Rewind 2015 Slither.io 2016 Snake 2017 Snake Pass 2017 Snake 2018

6 месяцев назад
How Deep Neural Networks Work

How Deep Neural Networks Work

A gentle introduction to the principles behind neural networks, including backpropagation. Rated G for general audiences. Follow me for announcements: https://twitter.com/_brohrer_ Visit the blog: https://brohrer.github.io/how_neural_networks_work.html Get the slides: https://docs.google.com/presentation/d/1AAEFCgC0Ja7QEl3-wmuvIizbvaE-aQRksc7-W8LR2GY/edit?usp=sharing

2 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
Google's self-learning AI AlphaZero masters chess in 4 hours

Google's self-learning AI AlphaZero masters chess in 4 hours

Google's AI AlphaZero has shocked the chess world. Leaning on its deep neural networks, and general reinforcement learning algorithm, DeepMind's AI Alpha Zero learned to play chess well beyond the skill level of master, besting the 2016 top chess engine Stockfish 8 in a 100-game match. Alpha Zero had 28 wins, 72 draws, and 0 losses. Impressive right? And it took just 4 hours of self-play to reach such a proficiency. What the chess world has witnessed from this historic event is, simply put, mind-blowing! AlphaZero vs Magnus Carlsen anyone? :) 19-page paper via Cornell University Library https://arxiv.org/abs/1712.01815 https://arxiv.org/pdf/1712.01815.pdf PGN: 1. e4 e5 2. Nf3 Nc6 3. Bb5 Nf6 4. d3 Bc5 5. Bxc6 dxc6 6. 0-0 Nd7 7. c3 0-0 8. d4 Bd6 9. Bg5 Qe8 10. Re1 f6 11. Bh4 Qf7 12. Nbd2 a5 13. Bg3 Re8 14. Qc2 Nf8 15. c4 c5 16. d5 b6 17. Nh4 g6 18. Nhf3 Bd7 19. Rad1 Re7 20. h3 Qg7 21. Qc3 Rae8 22. a3 h6 23. Bh4 Rf7 24. Bg3 Rfe7 25. Bh4 Rf7 26. Bg3 a4 27. Kh1 Rfe7 28. Bh4 Rf7 29. Bg3 Rfe7 30. Bh4 g5 31. Bg3 Ng6 32. Nf1 Rf7 33. Ne3 Ne7 34. Qd3 h5 35. h4 Nc8 36. Re2 g4 37. Nd2 Qh7 38. Kg1 Bf8 39. Nb1 Nd6 40. Nc3 Bh6 41. Rf1 Ra8 42. Kh2 Kf8 43. Kg1 Qg6 44. f4 gxf3 45. Rxf3 Bxe3+ 46. Rfxe3 Ke7 47. Be1 Qh7 48. Rg3 Rg7 49. Rxg7+ Qxg7 50. Re3 Rg8 51. Rg3 Qh8 52. Nb1 Rxg3 53. Bxg3 Qh6 54. Nd2 Bg4 55. Kh2 Kd7 56. b3 axb3 57. Nxb3 Qg6 58. Nd2 Bd1 59. Nf3 Ba4 60. Nd2 Ke7 61. Bf2 Qg4 62. Qf3 Bd1 63. Qxg4 Bxg4 64. a4 Nb7 65. Nb1 Na5 66. Be3 Nxc4 67. Bc1 Bd7 68. Nc3 c6 69. Kg1 cxd5 70. exd5 Bf5 71. Kf2 Nd6 72. Be3 Ne4+ 73. Nxe4 Bxe4 74. a5 bxa5 75. Bxc5+ Kd7 76. d6 Bf5 77. Ba3 Kc6 78. Ke1 Kd5 79. Kd2 Ke4 80. Bb2 Kf4 81. Bc1 Kg3 82. Ke2 a4 83. Kf1 Kxh4 84. Kf2 Kg4 85. Ba3 Bd7 86. Bc1 Kf5 87. Ke3 Ke6 Internet Chess Club (ICC) Software: Blitzin http://bit.ly/179O93N Discount Code: CHESSNETWORK I'm a self-taught National Master in chess out of Pennsylvania, USA who was introduced to the game by my father in 1988 at the age of 8. The purpose of this channel is to share my knowledge of chess to help others improve their game. I enjoy continuing to improve my understanding of this great game, albeit slowly. Consider subscribing here on YouTube for frequent content, and/or connecting via any or all of the below social medias. Your support is greatly appreciated. Take care, bye. :D ★ LIVESTREAM http://twitch.tv/ChessNetwork ★ FACEBOOK http://facebook.com/ChessNetwork ★ TWITTER http://twitter.com/ChessNetwork ★ GOOGLE+ http://google.com/+ChessNetwork ★ PATREON https://www.patreon.com/ChessNetwork ★ DONATE https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=QLV226E6FUUWG

10 месяцев назад